DEV Community: Hadil Ben Abdallah

React DataGrid: A Free, Open-Source React Data Grid with an Enterprise Edition (An AG Grid Alternative)

Hadil Ben Abdallah — Tue, 11 Aug 2026 09:09:04 +0000

If you've ever needed to build a serious data table in React, you've probably run into this problem. React doesn't include a native data grid component, so you're usually left choosing between paying for an enterprise solution or spending weeks building advanced table functionality yourself.

For small datasets, a simple HTML table works well enough. But once you need virtualization, row grouping, inline editing, advanced filtering, server-side data loading, or richer keyboard interactions, things become much more complicated. Those features require significant engineering effort if you decide to build everything from scratch.

That's exactly where React DataGrid, an open-source React data grid with both free and Enterprise offerings, comes in.

In this article, I'll walk through what makes a production-ready React data grid, introduce React DataGrid, show how to get started with its open-source edition, explore its standout features, explain what's available in the Enterprise edition, and compare it with AG Grid so you can decide which option is right for your project.

TL;DR

If you're looking for a React data grid that combines an open-source MIT-licensed core with an optional Enterprise edition, React DataGrid is worth considering.

It's an open-source React data grid built with React 18, TypeScript, and Tailwind CSS, designed for everything from simple data tables to large, data-intensive applications. The free edition provides a broad set of data-grid capabilities, while the Enterprise edition adds commercial features for teams that need more advanced workflows, data operations, and enterprise functionality.

Here's what React DataGrid offers:

✅ MIT-licensed, open-source core that is free to use.
✅ Virtual scrolling for datasets with 100,000+ rows and 200+ columns.
✅ Server-side infinite scrolling for datasets with 100M+ rows, including server-side filtering, sorting, pagination, intelligent block caching, request concurrency, and LRU cache eviction.
✅ Row grouping and aggregation for organizing and analyzing complex datasets.
✅ Tree Data for hierarchical rows and expandable parent-child structures.
✅ Market data support for applications that receive frequent live updates.
✅ 10 built-in themes, custom cell renderers, layout persistence, density modes, and extensive customization APIs.
✅ Accessibility support with WCAG 2.1 AA compliance and a published VPAT 2.4 report.
✅ Enterprise features including Server-Side Row Model, Master/Detail, Formula Engine, Undo/Redo, Range Selection, Clipboard (TSV), Fill Handle, Transactions, Cell Permissions, Audit Trail, Row Locking, Excel/CSV Import, PDF Export, Filter Presets, Saved Views, and Form Editor.
✅ A familiar AG Grid-style API that can make adoption easier for developers already working with enterprise data grid libraries.

What Are React Data Grid Components?

A React data grid component is a specialized table component designed to display and manage large, interactive datasets. Unlike a basic HTML table, it typically includes features such as sorting, filtering, pagination, row selection, inline editing, keyboard navigation, and virtualization.

A traditional HTML <table> is perfectly fine for displaying a few dozen rows of static data. The problem appears once datasets start growing or users need richer interactions.

Rendering thousands of rows can slow down the browser, and implementing advanced behaviors like filtering, editing, or drag-and-drop often means writing and maintaining a significant amount of custom code.

That's why most production React applications rely on dedicated data grid libraries. They solve performance challenges, provide a consistent user experience, and include many of the features that enterprise dashboards, admin panels, analytics platforms, and internal business applications depend on every day.

What Features Really Matter in a React Data Grid?

The difference between a basic data table and a production-ready React data grid comes down to five areas: performance, data operations, server-side capabilities, accessibility, and customization. These are also the features that many free libraries either limit or don't offer at all.

When evaluating a React data grid, here are the capabilities that deserve the most attention:

Performance at scale through virtual scrolling so tens of thousands of rows remain smooth to navigate.
Advanced data operations like row grouping, aggregation, filtering, multi-column sorting, and inline editing.
Server-side support for massive datasets using infinite scrolling, server-side filtering, sorting, and intelligent caching.
Accessibility with keyboard navigation, screen-reader compatibility, and WCAG compliance.
Customization through themes, custom cell renderers, layout persistence, and flexible APIs that fit your application's design system.

Many open-source React grids do one or two of these things well.

Performance might be excellent, but advanced grouping is missing. Another library may support editing but fall short when datasets become truly large.

React DataGrid was built to check every one of those boxes.

Introducing React DataGrid

React DataGrid is an open-source React data grid built with React 18, TypeScript, Tailwind CSS, and Vite. Its open-source core is MIT licensed and published as the react-open-source-grid npm package, while the project also offers an Enterprise edition with additional commercial features. The API is intentionally familiar to developers who have previously worked with AG Grid, which can make the learning curve smaller.

React DataGrid is part of the broader GridEngine platform, which provides data grid solutions for different technologies and use cases.

One thing I particularly like is that the project doesn't expect you to install it blindly. Before writing any code, you can explore a public live demo, browse the documentation, and inspect the GitHub repository to see exactly what the library offers.

That transparency is valuable, especially when you're evaluating a component that may become the backbone of your application's data layer.

Installation & Quick Start

Getting started with React DataGrid is straightforward. If you're starting a new React project or adding a data grid to an existing application, you can install the package and have a working grid running in just a few minutes.

The setup is intentionally minimal so you can focus on your data instead of configuration.

Install the package:

npm install react-open-source-grid

Then import the stylesheet:

import 'react-open-source-grid/dist/lib/index.css';

Finally, create your first grid:

import { DataGrid } from 'react-open-source-grid';
import type { Column, Row } from 'react-open-source-grid';

const columns: Column[] = [
  { field: 'id', headerName: 'ID', width: 70 },
  { field: 'name', headerName: 'Name', width: 180, editable: true },
];

const rows: Row[] = [
  { id: 1, name: 'John Doe' },
  { id: 2, name: 'Jane Smith' },
];

export default function App() {
  return (
    <DataGrid
      columns={columns}
      rows={rows}
    />
  );
}

From this simple starting point, you can gradually enable more advanced capabilities like grouping, virtual scrolling, custom renderers, server-side infinite scrolling, and layout persistence, while keeping the same underlying grid as your application grows.

Core Features of React DataGrid

React DataGrid stands out among open-source React data grid libraries because it doesn't just offer a long feature checklist. The open-source core gives you a broad foundation for everyday data-intensive applications, while the Enterprise edition provides additional capabilities for more advanced workflows as your requirements evolve.

Let's look at the capabilities that make the biggest difference in real-world React applications.

Everyday Grid Features You'll Actually Use

Every data grid should make everyday interactions feel effortless, and React DataGrid's open-source core covers a broad set of essential capabilities without requiring additional plugins or a commercial license.

Out of the box, you get:

Click-to-sort columns
Per-column filtering with advanced filtering options
Pagination with 10, 20, or 50 rows per page
Drag-to-resize columns
Drag-and-drop column reordering
Sticky column headers
Full keyboard navigation
Single and multiple row selection
Double-click inline editing
Built-in quick filter for searching across the entire grid

Beyond those essentials, React DataGrid also includes several quality-of-life features that become valuable in larger applications. APIs such as rowStyle, rowClass, and getRowHeight make it easy to highlight overdue invoices, completed tasks, priority tickets, or any other rows that deserve extra visual attention.

React DataGrid live demo with filtering, sorting, and status badges

Row Grouping and Aggregation for Complex Data

Row grouping is one of those features that developers often don't realize they need until users ask for it. When datasets become more complex, grouping records by department, status, region, customer, or category makes analysis much easier than scrolling through hundreds or thousands of individual rows.

React DataGrid lets users simply drag a column into the grouping area to organize data into expandable groups. Beyond grouping itself, the library also supports aggregation footers at two different levels.

You can display subtotals for each individual group as well as a grand-total footer for the entire dataset, with built-in aggregation functions including:

Sum
Average
Minimum
Maximum
Count

Both group-level and grid-level aggregation are included in the open-source grid. That's noteworthy because similar functionality is often associated with commercial data grid offerings.

Besides row grouping, React DataGrid also supports tree data, allowing you to display hierarchical datasets with expandable parent and child rows. This is useful for cases such as organizational charts, file explorers, category trees, or nested project structures.

React DataGrid Tree Data Demo for folder and file structure with nested directories

Built for Scale: Virtual Scrolling, Market Data, and Server-Side Infinite Data

Performance is often the first thing users notice when working with large datasets. A data grid can have every feature imaginable, but if scrolling feels sluggish or interactions lag, the overall experience quickly suffers.

React DataGrid tackles this challenge with two complementary approaches, depending on the size of your data.

For large datasets that can still live in the browser, the library supports virtual scrolling. Instead of rendering every row and column into the DOM, it only renders what's currently visible on screen.

This enables smooth performance with 100,000+ rows and more than 200 columns, while reducing rendering overhead and memory usage compared to rendering everything at once. The documentation also reports up to 100× faster rendering, 100× lower memory usage, and smooth 60 FPS scrolling in virtual mode.

React DataGrid Virtual Scrolling Demo with 100,000 rows & 200 columns

Performance isn't only about handling large datasets; it also matters when the data is constantly changing. React DataGrid includes support for real-time market data cases, making it suitable for applications that receive frequent live updates while remaining responsive.

If you're building financial dashboards, trading platforms, monitoring systems, or live analytics applications, the grid is designed to process continuous data updates while preserving smooth scrolling and a responsive user experience. This makes it a good option for applications where users need to monitor rapidly changing information without sacrificing performance.

React DataGrid live streaming updates Demo

For applications dealing with truly massive datasets, React DataGrid goes a step further with its free Server-Side Infinite Scroll capability. The InfiniteScrollDataGrid component works with a ServerSideDataSource to request only the data users actually need while scrolling, instead of loading millions of records into the browser at once.

This free server-side mode includes:

Server-side filtering, sorting, and pagination
Intelligent block caching
Configurable request concurrency
LRU cache eviction
Efficient handling of datasets with 100 million or more rows

Despite all of those capabilities, the API is simple:

const dataSource = new ServerSideDataSource({
  blockSize: 100,

  getRows: async (request) => {
    const response = await fetch("/api/data", {
      method: "POST",
      body: JSON.stringify(request),
    });

    return response.json();
  },
});

Instead of forcing developers to build custom pagination or infinite loading logic, the component handles the heavy lifting while your backend simply returns the requested records.

For applications that need more advanced server-side data-management capabilities, React DataGrid also offers a separate Server-Side Row Model as part of its Enterprise features.

Enterprise Features for Advanced Workflows

React DataGrid also offers an Enterprise edition for teams that need capabilities beyond the open-source core.

The Enterprise tier adds a collection of advanced features designed for more sophisticated data workflows, including server-side data management, transactional editing, controlled access, auditability, data import and export, and advanced record management.

Some of the Enterprise features include:

Server-Side Row Model for applications that need more advanced server-side data management and block-based data fetching for large datasets.
Master/Detail for expanding records and displaying related or nested information.
Formula Engine for Excel-style formulas directly inside grid cells.
Undo/Redo for multi-step reversible edits.
Range Selection for selecting Excel-style cell ranges.
Clipboard (TSV) for copying and pasting tabular data with spreadsheet-style workflows.
Fill Handle for dragging to fill or copy cell values across a range.
Transactions for staged add, update, and remove operations.
Cell Permissions for controlling per-cell editing and data visibility.
Audit Trail for maintaining an immutable record of edits.
Row Locking for collaborative workflows where specific rows need to be locked.
Excel/CSV Import for mapping, validating, and coercing imported data.
PDF Export for generating branded, paginated documents from grid data.
Filter Presets with an AND/OR filter builder and reusable filtering configurations.
Saved Views for storing personal or shared grid layouts.
Form Editor for editing records through a dedicated slide-in form interface.

These features are particularly useful when a data grid starts acting as an interactive workspace for business operations.

For example, a financial application might use the Formula Engine and Range Selection for spreadsheet-like workflows, while a business application with stricter data controls could combine Transactions, Cell Permissions, Audit Trail, and Row Locking to provide more controlled data management.

The Enterprise edition therefore gives teams a path to move from a capable open-source data grid to a more advanced commercial data-management experience without having to replace the underlying grid technology.

The open-source core also includes several features that make day-to-day development easier, such as computed value getters, custom formatters, flexible column sizing, conditional cell styling, shared default column definitions, built-in tooltips, loading overlays, and "no rows" states.

Taken together, the open-source core and Enterprise edition give React DataGrid a broader range of capabilities while allowing teams to choose the level of functionality that matches their requirements.

Accessibility and Enterprise Compliance

Accessibility is an area where many open-source UI libraries still fall short. Keyboard navigation may be partially implemented, screen readers may receive limited support, and formal accessibility documentation is often missing altogether.

React DataGrid stands out by treating accessibility as a core feature, not just an afterthought.

The grid is WCAG 2.1 AA compliant and includes full keyboard navigation, ARIA support, and screen-reader compatibility. It also publishes a VPAT 2.4 accessibility report along with Section 508 documentation, making it much easier for organizations with accessibility requirements to evaluate React DataGrid before adopting it.

While individual developers certainly benefit from accessible components, this kind of documentation becomes more valuable in larger companies where procurement, legal, or compliance teams often review third-party dependencies before they're approved for production use.

Theming, Customization, and Developer Experience

A data grid rarely exists in isolation. It needs to blend naturally into the rest of your application's design system, support custom business logic, and remain flexible as requirements evolve.

React DataGrid provides 10 built-in themes, covering both light and dark interfaces, including Quartz, Alpine, Material, Nord, Dracula, Solarized (Light and Dark), Monokai, One Dark, and a standard Dark theme.

Theme switching is powered by CSS variables, making it easy to match the rest of your application without extensive styling work. Themes can be switched instantly while preserving consistent spacing, typography, and component styling.

React DataGrid theme system

Customization goes well beyond colors. The library also provides:

A custom cell renderer framework for badges, progress bars, images, buttons, charts, and icons.
A right-click context menu with actions like copy, export, pin/unpin, auto-size, hide columns, and filter by value.
Layout persistence using localStorage, server storage, or user profile storage.
Multiple density modes ranging from Ultra Compact to Comfortable.
Value getters and formatters for computed columns.
Conditional styling through cellStyle, cellClass, and cellClassRules.
Shared column configuration with defaultColDef and built-in default sorting.
Tooltips, loading overlays, and no-data overlays.
Declarative column visibility for showing or hiding columns based on user roles or application state.

Beyond the getting-started guides, the project includes a documented Grid API with more than 100 methods, along with interactive examples demonstrating how those APIs work. Enterprise capabilities build on this foundation with additional functionality for advanced data workflows.

How React DataGrid Compares to AG Grid

AG Grid Community is a solid open-source grid, but many of its most powerful capabilities, including row grouping, server-side data handling, pivoting, and several advanced data operations, are reserved for the commercial Enterprise edition.

React DataGrid takes a different approach by making many of those enterprise-style features available under the MIT license.

Here's a side-by-side comparison:

Feature	AG Grid Community	AG Grid Enterprise ($$)	React DataGrid Free	React DataGrid Enterprise ($$)
License	MIT	Commercial	MIT	Commercial
Virtual Scrolling	✅	✅	✅	✅
Tree Data	❌	✅	✅	✅
Row Grouping	❌	✅	✅	✅
Server-Side Infinite Scroll	❌	✅	✅ (100M+ rows)	✅
Server-Side Row Model	❌	✅	❌	✅
Aggregation Footers	❌	✅	✅	✅
Context Menus	❌	✅	✅	✅
Advanced Filtering	Basic	✅	✅	✅
Faceted Search / Token Search	❌	✅	✅	✅
Row Pinning	❌	✅	✅	✅
Master/Detail Rows	❌	✅	❌	✅
Pivot Table	❌	✅	✅	✅
Integrated Charts	❌	✅	✅	✅
Advanced Cell Editors	❌	✅	✅	✅
Excel Export	❌	✅	❌	✅
Formula Engine	❌	❌	❌	✅
Undo / Redo	❌	❌	❌	✅
Range Selection	❌	❌	❌	✅
Clipboard (TSV)	❌	❌	❌	✅
Fill Handle	❌	❌	❌	✅
Transactions	❌	❌	❌	✅
Cell Permissions	❌	❌	❌	✅
Audit Trail	❌	❌	❌	✅
Row Locking	❌	❌	❌	✅
Excel / CSV Import	❌	❌	❌	✅
PDF Export	❌	❌	❌	✅
Filter Presets	❌	❌	❌	✅
Saved Views	❌	❌	❌	✅
Form Editor	❌	❌	❌	✅
Accessibility (WCAG 2.1 AA + VPAT)	Partial	✅	✅	✅
Built-in Themes	1	Multiple	10	10

This doesn't necessarily mean React DataGrid should replace AG Grid in every project. AG Grid remains one of the most mature data grid solutions available and comes with commercial support for organizations that require enterprise SLAs.

However, if your team needs a capable open-source React data grid with features such as grouping, aggregation, virtualization, theming, and rich customization, React DataGrid's MIT-licensed core presents a compelling option.

When requirements expand to advanced workflows, auditing, permissions, transactions, or spreadsheet-style operations, the Enterprise edition provides an upgrade path without requiring you to switch to a different grid component.

Who Should Use React DataGrid?

React DataGrid isn't trying to be the right data grid for every React project, and that's actually one of its strengths.

I think it's well suited for:

Internal business dashboards
Admin panels
SaaS applications
CRM and ERP systems
Financial dashboards
Analytics platforms
Inventory management systems
Applications that need grouping, aggregation, virtualization, rich customization, or Server-Side Infinite Scroll for large datasets without paying for a commercial grid
Enterprise applications that may need advanced capabilities

If your application starts with a few hundred rows today but could eventually grow into tens of thousands, or even millions, of records, it's nice to know you won't need to replace your grid component later just because your data has outgrown it.

📌 If you find this useful, consider starring the GitHub repo; it helps support the open-source project and its continued development.

⭐ Star React DataGrid on GitHub

Final Thoughts

What stands out about React DataGrid isn't a single headline feature; it's the combination of an open-source core and an optional Enterprise edition that can grow with an application's requirements.

The MIT-licensed core provides developers with a capable React data grid for everyday data management, including sorting, filtering, grouping, aggregation, virtualization, theming, customization, and accessibility.

For teams that need more advanced workflows, the Enterprise edition adds capabilities such as Server-Side Row Model, Master/Detail, Formula Engine, Transactions, Cell Permissions, Audit Trail, Row Locking, Excel/CSV Import, PDF Export, Filter Presets, Saved Views, and Form Editor.

That gives teams a straightforward choice:

Start with the free, open-source grid for projects that don't need commercial features
Move to the Enterprise edition when more advanced data operations and workflows become necessary.

This article focused on what React DataGrid offers and how its free and Enterprise capabilities compare. In my next article, I'll take a different approach by building a real React project with it, sharing the complete development experience, highlighting what worked well, and discussing any challenges I encounter along the way. Stay tuned!

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

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Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

AI Agents for Marketing in 2026: 8 Platforms Replacing Manual Marketing Ops

Hadil Ben Abdallah — Wed, 05 Aug 2026 09:02:32 +0000

According to HubSpot's 2026 State of Marketing Report, 61% of marketers believe AI is creating the biggest disruption marketing has experienced in the last 20 years, while 80% of marketers are already using AI for content creation and 75% are using it for media production. The shift is all about how effectively marketing teams can turn AI from a productivity assistant into an autonomous execution layer.

That shift explains why AI agents for marketing (also called autonomous marketing platforms) have become one of the fastest-growing categories in marketing technology.

Unlike AI assistants that simply generate blog posts, suggest campaign ideas, or summarize performance reports, AI marketing agents are designed to monitor real-time signals, make decisions, execute workflows, optimize campaigns, and continuously adapt based on outcomes.

For growth teams trying to increase output without constantly expanding their operations team, this represents a fundamental change.

In this guide, we'll look at the eight best AI agents for marketing in 2026, compare where each platform fits, examine their limitations, and explore which solutions can replace parts of the manual marketing operations workload.

Quick Comparison: Best AI Agents for Marketing in 2026

Before diving into each platform, here's a side-by-side comparison of the leading AI marketing agents and autonomous marketing platforms available today.

Platform	Category	Best For	Pricing	Key Limitation
Jasper AI	AI content generation	Scaling blogs, ads, email campaigns, and brand-consistent content	Paid	Generates content but doesn't execute campaigns or react to marketing signals
Hellyeah	AI-powered marketing platform	Teams wanting autonomous paid media operations and AI-powered marketing workflows	Enterprise	Requires onboarding, event instrumentation, and setup before delivering value
Smartly.io	Paid social automation	Large paid social teams managing Meta and TikTok campaigns	Paid / Enterprise	Focused on paid social only
HubSpot AI	CRM + AI marketing suite	SMBs wanting CRM, email marketing, and AI assistance in one platform	Free / Paid / Enterprise	AI assists users instead of operating autonomously
Persado	AI messaging optimization	Enterprise email and advertising teams	Enterprise	Focuses only on copy optimization
Clay	AI outbound personalization	B2B sales and outbound marketing	Paid	Outbound-focused rather than full marketing operations
Anyword	AI writing + predictive scoring	Teams optimizing marketing copy before publishing	Free / Paid	Requires humans to approve and publish content
n8n	Workflow automation + AI agents	Engineering-led organizations building custom AI workflows	Free / Paid	Requires technical resources to build and maintain

Although every platform on this list uses AI, they don't solve the same problem.

Understanding the differences between them is important because the right choice depends far more on what part of your marketing operation you want to automate than on which platform has the most AI features.

What "Replacing Manual Marketing Ops" Means

When people hear that AI agents are replacing marketing operations, it's easy to assume the goal is to replace marketers themselves. In reality, that's not what these platforms are designed to do.

Marketing teams still need people to:

define positioning
understand customers
shape brand strategy
approve messaging
make business decisions

Those responsibilities depend on creativity, judgment, and market knowledge that AI cannot replace.

What AI increasingly takes over is everything that happens after those strategic decisions have already been made. Once a campaign has been planned, someone still needs to launch it, build audience segments, monitor bids, rotate creatives, prepare reports, analyze experiments, watch for churn signals, schedule lifecycle campaigns, and research prospects before outbound outreach begins.

Individually, none of those activities are especially difficult. Together, however, they consume a significant portion of a marketing team's week, leaving less time for strategy and growth initiatives. AI marketing agents are designed to automate that execution layer so marketers spend less time operating systems and more time making decisions that actually move the business forward.

Instead of requiring someone to manually monitor dashboards throughout the day, these platforms continuously observe performance signals, decide what action makes sense based on current conditions, execute that action automatically, and evaluate the results before making future decisions.

That creates three broad categories of AI marketing platforms.

Category	What It Does	Examples
Single-function AI agents	Automate one marketing operation exceptionally well	Jasper AI, Clay, Persado, Anyword
Multi-function marketing platforms	Combine several connected marketing capabilities into one platform	HubSpot AI, Smartly.io
Full-stack agentic marketing platforms	Coordinate AI-powered marketing workflows and paid media operations from a unified platform	Hellyeah

The distinction matters because many products now advertise themselves as AI-powered, even though they still function primarily as assistants that help marketers work faster.

True AI marketing agents go much further by operating independently once objectives, constraints, and data are in place, allowing teams to automate entire operational workflows.

1. Jasper AI

One of the biggest challenges marketing teams faced when generative AI became mainstream wasn't simply creating content; it was producing large volumes of content without losing consistency across different writers, campaigns, and channels.

That's the problem Jasper AI is built to solve.

Jasper allows organizations to train a Brand Voice using their existing content. Once that foundation is established, the platform can generate blog posts, landing pages, email campaigns, advertising copy, product descriptions, and social media content that closely matches the company's writing style.

Its Campaigns feature also helps teams create multiple marketing assets from a single campaign brief, making it much easier to produce coordinated content for different channels without starting from scratch each time. For organizations publishing at scale, that significantly reduces production time while maintaining a consistent brand voice across the entire marketing team.

Best for

Marketing teams, content marketers, and demand generation teams producing large volumes of written marketing content across multiple channels.

Limitation

Jasper excels at generating content, but it doesn't operate your marketing stack. It won't manage advertising campaigns, monitor customer behavior, optimize budgets, trigger lifecycle campaigns, or react to performance signals automatically.

Pricing

Paid.

2. Hellyeah

Many companies don't struggle because they're missing marketing tools; they struggle because every marketing function lives inside a different one. Paid advertising, content operations, analytics, and reporting often run across separate platforms, leaving someone on the team responsible for connecting everything, interpreting the data, and managing the day-to-day execution that keeps campaigns running.

Unlike traditional marketing platforms that gradually added AI features over time, Hellyeah AI is an AI-native growth engine designed to reduce marketing operations overhead. Rather than simply helping marketers complete individual tasks faster, it automates repetitive marketing execution so teams can spend less time managing campaigns and more time focusing on strategy, creativity, and growth.

Today, Hellyeah centers on two core products. AIMA continuously optimizes paid media by monitoring performance, adjusting bids, reallocating budgets, and responding to conversion signals, while CLI provides an AI-powered interface for interacting with marketing workflows and operational data more efficiently. Together, they help reduce repetitive marketing work while keeping marketers in control of business decisions.

AIMA: Replacing Daily Paid Media Operations

Managing paid advertising requires constant optimization. Budgets need to be reallocated, bids adjusted, creatives refreshed, and audience performance monitored as campaign data changes throughout the day. For growing marketing teams, keeping up with those decisions manually can quickly become a full-time job.

AIMA automates that optimization layer by continuously monitoring campaign performance, interpreting conversion signals, adjusting bidding strategies, reallocating budgets, and helping teams respond to changing campaign performance. Marketing teams remain responsible for campaign strategy and business goals, while AIMA handles much of the repetitive execution required to keep campaigns performing efficiently.

CLI: AI-Powered Marketing Operations

Hellyeah's CLI provides an AI-first interface for interacting with marketing workflows, making it easier to execute operational tasks without navigating multiple dashboards or manually coordinating different systems. Instead of relying solely on traditional interfaces, teams can use AI to access marketing capabilities more efficiently and streamline day-to-day operations.

Combined with AIMA, the CLI helps reduce the operational overhead of running modern marketing by making routine execution faster and more accessible, while marketers continue focusing on strategy, messaging, and business decisions.

Best For

Growth-stage SaaS companies, enterprise B2B organizations, e-commerce businesses, fintech companies, gaming companies, mobile apps, and education platforms that want to reduce marketing operations overhead through AI-powered paid media optimization and modern AI-driven marketing workflows.

Limitation

Hellyeah is not a plug-and-play platform that delivers full autonomous execution on the first day.

Like any serious agentic marketing system, it requires clean event instrumentation, clearly defined business objectives, and an onboarding period so the platform can understand your business objectives, campaign structure, and operational workflows. Teams willing to invest in that foundation gain a platform that becomes more effective as more operational data becomes available over time, but organizations expecting immediate results without preparation may find the initial setup more involved than simpler marketing tools.

Pricing

Enterprise.

3. Smartly.io

Managing paid social campaigns at an enterprise scale involves much more than launching advertisements. Marketing teams constantly monitor campaign performance, adjust bids, test audiences, rotate creative assets, optimize budgets, and compare results across multiple advertising platforms. As campaign volume grows, those operational responsibilities quickly become too time-consuming for manual management alone.

Smartly.io helps automate much of that workload by combining creative production, campaign management, audience testing, bid optimization, and performance analysis inside a single platform. Its AI capabilities identify high-performing creative combinations, optimize campaign delivery across channels such as Meta and TikTok, and automatically allocate additional budget toward stronger-performing campaigns as new performance data becomes available.

The platform generates copy for landing pages, paid advertisements, emails, product descriptions, social media posts, and other marketing assets while assigning predictive performance scores to every variation. Those predictions are based on historical marketing data, giving teams another layer of confidence when comparing headlines, calls to action, subject lines, and advertising copy before launching a campaign.

While those scores don't replace real-world testing, they can help marketers prioritize stronger candidates earlier in the content creation process and reduce the number of weak variations that ever reach production.

Best for

Marketing teams producing high volumes of advertising, website, email, and social media copy that want performance insights before publishing.

Limitation

Anyword remains an AI-assisted writing platform rather than an autonomous marketing system. Human marketers still decide which content to approve, edit, publish, and distribute, and the platform doesn't execute campaigns or optimize marketing operations after the content has been created.

Pricing

Free / Paid.

8. n8n + AI Nodes

Not every company wants an opinionated marketing platform with predefined workflows. Many engineering-led organizations prefer building their own automation infrastructure so it integrates perfectly with existing internal systems, data sources, and business logic.

That's where n8n stands out.

As an open-source workflow automation platform, n8n connects hundreds of services through visual workflows while allowing developers to incorporate LLM-powered AI nodes into decision-making processes. Teams can build custom AI-powered workflows for campaign approvals, lead routing, CRM synchronization, reporting, content production pipelines, customer notifications, and countless other marketing operations.

Because the platform is self-hostable and highly customizable, organizations retain full ownership over their automation stack instead of relying entirely on a managed SaaS platform. That flexibility makes n8n especially attractive for companies with strong engineering teams and complex internal requirements.

Best for

Engineering-led organizations that want to design, build, and maintain highly customized AI-powered marketing workflows using their own infrastructure.

Limitation

The flexibility that makes n8n so powerful also makes it more demanding to implement. Building, maintaining, debugging, and continuously improving AI workflows requires dedicated engineering resources, making it less suitable for marketing teams looking for a ready-to-deploy autonomous marketing platform.

Pricing

Free / Paid.

How to Evaluate an AI Marketing Agent Platform

Not every platform marketed as an AI marketing solution is really an AI agent.

Many products still function as AI assistants that generate recommendations while leaving marketers responsible for execution. True AI agents go much further by observing real-time signals, making decisions, carrying out those decisions, and continuously improving based on the outcomes they observe.

If you're evaluating an AI agent for marketing, these are the questions that matter most.

Evaluation Criterion	What to Ask	Red Flag
Autonomy	Does the platform make decisions autonomously or simply recommend actions?	It only suggests optimizations that humans must review and approve.
Coverage	Does it automate one marketing function or multiple connected operations?	It automates a single workflow while claiming to replace an entire marketing team.
Real-Time Signals	Does it continuously monitor product, CRM, behavioral, and advertising data?	It relies on scheduled imports, CSV uploads, or delayed reporting.
Learning Loop	Does the system improve future decisions based on previous outcomes?	Performance improves only when humans manually update rules or workflows.
Setup Requirements	What onboarding, instrumentation, and data preparation are required before deployment?	It promises complete autonomous marketing within minutes and without preparation.

When campaign performance, customer behavior, experimentation results, and workflow execution all inform one another, improvements don't remain isolated inside individual tools. Instead, every successful outcome strengthens future decisions across the entire marketing operation, creating a compounding effect that's difficult to achieve with disconnected software.

Frequently Asked Questions

What are AI agents for marketing?

→ AI agents for marketing (also called autonomous marketing platforms) are AI systems that continuously monitor marketing signals, make decisions, execute campaigns, and learn from the outcomes without requiring marketers to initiate every action manually. Unlike traditional automation platforms that follow predefined rules, AI agents adapt their behavior as new data becomes available.

Can AI agents replace a marketing team?

→ Not entirely.

AI agents are designed to replace much of the marketing execution layer, including campaign optimization, reporting, audience management, experimentation, lifecycle automation, and many repetitive operational tasks. Human marketers remain responsible for strategy, positioning, creative direction, customer research, brand management, and the business decisions that require judgment, context, and creativity.

What's the difference between marketing automation and AI marketing agents?

→ Traditional marketing automation executes workflows that humans configure in advance using predefined rules.

AI marketing agents continuously evaluate current conditions, decide which action is most appropriate, execute that action automatically, and refine future decisions based on the results they observe. In other words, automation follows instructions, while AI agents determine what the next instruction should be.

Which AI marketing platform is best in 2026?

→ The answer depends on the part of marketing you're trying to automate.

If your priority is AI-assisted content generation, Jasper AI and Anyword are good choices. Clay is a strong option for outbound personalization, while Smartly.io remains one of the leading platforms for paid social automation.

If you're looking for a purpose-built platform that brings paid acquisition, lifecycle marketing, continuous experimentation, and custom workflows together into one connected autonomous system, Hellyeah is the strongest fit because it was designed specifically to automate the broader marketing operations layer rather than a single function.

Final Thoughts

Marketing teams aren't adopting AI agents simply because they want to work faster.

They're adopting them because modern marketing operations have become too complex for people to monitor every campaign, workflow, customer signal, and experiment manually throughout the day.

The biggest shift isn't that AI writes content or recommends optimizations. It's that an increasing share of the operational work, monitoring campaigns, adjusting budgets, reacting to customer behavior, coordinating workflows, and continuously testing new ideas, can now happen autonomously while marketers focus on higher-value decisions.

The right platform ultimately depends on what you're trying to automate. Some teams need better content creation, others need smarter outbound prospecting or paid media optimization, while larger organizations may benefit from platforms that automate broader marketing operations.

Understanding those differences will help you choose a solution that fits your workflow instead of adding another disconnected AI tool to your stack.

Thanks for reading! 🙏🏻 Please follow Hadil Ben Abdallah & Hellyeah for more 🧡

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Hellyeah is an autonomous AI growth platform that runs and optimizes marketing operations in real time. It helps companies scale faster by turning their entire growth engine into a continuously learning, always-on system.

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Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

5 Most Important Programming Languages to Learn in 2026 (Based on Real Industry Demand)

Hadil Ben Abdallah — Mon, 03 Aug 2026 09:05:57 +0000

Every year, developers ask the same question:

"Which programming language should I learn next?"

And every year, the internet gives us a hundred different answers.

One person says AI will replace programmers, so learning any language is pointless. Meanwhile, others are arguing that you don't even need to learn programming anymore because AI already writes half the code. (I actually shared my thoughts on that in my article about whether you should still learn coding in the age of AI)

Then you have another group telling you to learn the newest language everyone is talking about, while companies are still hiring developers who know technologies that have been around for years.

So what is really worth learning in 2026?

Not the language with the loudest hype.

Not the one that gets the most viral posts.

The languages that matter are the ones powering the things companies are building right now: AI systems, cloud infrastructure, mobile apps, enterprise software, and high-performance applications.

After looking at where the industry is moving, these are the five programming languages I believe deserve your attention in 2026:

Python
Go
Rust
Zig
Java (plus Kotlin)

If you're looking for your first developer job or planning your next career move, these languages are among the best programming languages to learn in 2026.

Let's talk about why.

1. Python: Still the Language Behind the AI Revolution

Yes, Python again.

Some developers might roll their eyes because Python has been on every "languages to learn" list for years.

But there is a reason it keeps coming back.

Python is no longer just a beginner-friendly programming language. In 2026, it has become one of the main building blocks of the AI ecosystem.

Almost every major AI workflow involves Python somewhere:

Machine learning models
AI agent frameworks
Data processing pipelines
Automation scripts
Web scraping tools
LLM applications

If you are building something with artificial intelligence today, chances are you will eventually touch Python.

But here is the important part:

Knowing Python syntax alone is not impressive anymore.

Almost everyone can create a small Python project and upload it to GitHub.

The real value comes from what you build with it.

A developer who knows Python and understands AI systems, automation, backend development, or data workflows has a stronger advantage than someone who only knows basic syntax.

Python is not exciting because it is new.

It is exciting because it keeps becoming useful in new areas.

2. Go: The Language Running the Cloud

Go is probably not the language you see trending every week on social media.

There are no endless debates about Go syntax. There are fewer flashy tutorials compared to JavaScript or Python.

But behind the scenes, Go is everywhere.

A huge amount of modern cloud infrastructure depends on it.

If you have ever used technologies like containers, cloud platforms, or Kubernetes-based systems, you have probably interacted with software written in Go.

The reason is simple:

Go was designed for modern infrastructure.

It handles concurrency extremely well, making it a great choice for systems that need to process lots of tasks at the same time without turning into a maintenance nightmare.

Companies love it because it helps developers build:

Fast backend services
Cloud-native applications
Developer tools
Distributed systems

Go might not be the most exciting language to talk about.

But when companies need reliable systems that scale, Go keeps appearing.

And that is usually a good sign for job opportunities.

3. Rust: Performance Without Giving Up Safety

Rust is the language that many developers want to learn but hesitate to start.

And honestly, the reason is understandable.

Rust has a reputation for being difficult.

The ownership system and borrow checker can feel confusing when you first encounter them.

You might spend your first few weeks asking:

"Why is the compiler fighting me?"

But after that initial learning curve, Rust starts making more sense.

The biggest advantage of Rust is that it combines two things developers usually have to choose between:

Low-level performance
Memory safety

Traditionally, languages like C and C++ gave developers incredible control and speed, but that power also came with many risks.

Rust tries to provide that same level of performance while preventing many common memory-related problems before the program even runs.

That is why Rust is appearing in areas like:

Operating systems
Browser engines
Infrastructure tools
Security-focused applications
High-performance backend services

It is also becoming one of the most respected languages among developers.

Rust is not the easiest language to learn.

But for developers who want to work closer to the hardware or build extremely reliable systems, it is one of the most interesting choices in 2026.

4. Zig: The Newcomer Developers Should Watch

Now let's talk about the language that many developers are still discovering:

Zig.

Zig is not trying to replace every programming language.

Instead, it is targeting a specific problem:

How can we make low-level programming simpler and more enjoyable?

Zig takes inspiration from C but tries to remove some of its frustrations.

The idea is simple:

Give developers more control without adding unnecessary complexity.

One interesting feature is Zig's compile-time execution, which allows developers to run certain logic while compiling instead of waiting until the program runs.

This can make systems more efficient and predictable.

Zig still has a smaller ecosystem than languages like Python, Java, or Go.

But it has been gaining attention because developers are interested in alternatives for:

Systems programming
Embedded development
Performance-focused applications

It might not be the language you need to learn tomorrow.

But it is definitely one worth watching.

Sometimes the languages that quietly grow today become the technologies everyone talks about later.

5. Java and Kotlin: The Languages That Refuse to Disappear

Every time someone creates a "modern programming languages" list, someone asks:

"Java? Really?"

Java has been around for decades, but that does not mean it is outdated.

A huge amount of the world's software still depends on it.

Banks, insurance companies, large enterprises, and critical business systems continue running on Java because stability matters.

Companies don't rebuild massive systems from scratch just because a newer language becomes popular.

They choose technologies that are reliable, maintainable, and proven.

At the same time, Java's ecosystem has continued evolving.

For Android development, Kotlin has become the preferred language and is deeply integrated into modern Android workflows.

Kotlin also expanded beyond mobile with Kotlin Multiplatform, allowing developers to share code across different platforms.

So while Java represents stability, Kotlin represents a more modern direction.

Together, they remain extremely relevant in 2026.

So Which Programming Language Should You Learn in 2026?

It depends on what you want to build.

If you want to work in AI, automation, or data:

Start with Python.

If you want backend development and cloud infrastructure:

Learn Go.

If you want performance, security, and systems programming:

Learn Rust.

If you are interested in low-level programming and want to explore something newer:

Keep an eye on Zig.

If you want enterprise development or Android:

Java and Kotlin are still excellent choices.

There is no single "best" programming language.

The best language is the one that helps you build something useful.

Final Thoughts

The programming world changes quickly.

A language that looks unstoppable today can become irrelevant tomorrow, and a language that seems "boring" can quietly power some of the biggest systems in the world.

That is why choosing what to learn in 2026 is not about chasing every new trend. It is about understanding where technology is moving and picking the tools that match the problems you want to solve.

But you should always keep in mind that learning a programming language is only the beginning.

A developer who understands one language deeply and knows how to solve problems will always be more valuable than someone who knows ten languages only at a beginner level.

💬 Now I want to hear your opinion.

Which programming language do you think will matter the most in 2026?

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

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Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

24 Best AI Design Tools for Designers and Developers in 2026

Hadil Ben Abdallah — Thu, 30 Jul 2026 09:04:11 +0000

Not long ago, designers relied on AI mostly for generating images or experimenting with quick concepts. Today, AI design tools can generate complete user interfaces, create interactive applications, write production-ready code, build marketing assets, and even help developers move from a Figma file to a working product much faster than before.

That rapid evolution has also created a new problem.

Search for AI design tools, and you'll find hundreds of recommendations. And looking at feature lists alone doesn't tell you which one fits your requirements.

That's why I tested them myself.

Instead of creating another roundup based on documentation, I spent time using 24 popular AI design tools across different categories. Whenever tools shared the same purpose, I evaluated them using the same prompt. For workflow-focused platforms that solve different problems, I created individual prompts that reflected their primary use case.

The result is a practical guide for designers, developers, product managers, and founders who want to understand what each tool does and which one deserves a place in their workflow.

If you're searching for the best AI design tools, AI coding tools, AI website builders, or AI tools for designers and developers, this guide will help you choose the right platform.

TL;DR

After testing all 24 AI design tools, these are the platforms that stood out for different workflows:

Most complete AI UI design workflow: Flowstep → Multi-screen generation, editable canvas, React + TypeScript export, and MCP integration.
Fastest way to build an AI web app: Lovable → Quickly turns prompts into functional applications.
Strongest tool for marketing assets: Canva AI → Fast content creation with an easy editing workflow.
Highest-quality AI illustrations: Leonardo AI → Detailed concept art with excellent realism.
Most capable AI coding assistant: Cursor → Understands entire projects instead of individual files.

The best choice ultimately depends on your workflow.

What Are AI Design Tools?

AI design tools are software powered by artificial intelligence that help designers and developers create digital products more efficiently. Depending on the tool, AI can generate interfaces from text prompts, produce marketing graphics, create illustrations, write frontend code, build complete web applications, recommend color palettes, analyze user experiences, or automate repetitive design tasks.

One reason these platforms have become so popular is that they reduce the time spent on routine work. Instead of building every layout from scratch or manually writing boilerplate code, teams can start with AI-generated foundations and spend more time refining ideas and solving real product problems.

Today's AI design software generally falls into several categories.

AI UI Design & Code Export: Generating complete user interfaces that can be exported to Figma or converted into React components
AI App & Website Builders: Producing functional applications instead of static mockups.
AI Graphic Design & Visual Content: Creating branding assets
AI image generators: Producing original visuals
AI Coding Agents & Dev Tools: Assist developers during implementation
Design Workflow & UX Tools: Improve research, testing, and product planning.

These tools are not here to replace designers or developers; they are becoming productivity multipliers. They handle repetitive work, accelerate prototyping, and shorten the gap between an idea and a working product, allowing teams to iterate much faster.

How I Evaluated These AI Design Tools

Comparing AI design tools isn't as simple as asking each platform to generate a nice-looking screen.

These tools solve different problems, so judging all of them using the same expectations wouldn't produce a fair comparison.

Instead, I grouped the tools by category and evaluated each one according to the type of work it was designed to do. Whenever multiple tools shared the same purpose, I tested them using the same prompt without modifying the requirements. For categories where every platform serves a different role, such as UX research or workflow automation, I created prompts tailored to each tool's primary use case.

Throughout the testing process, I focused on the factors that matter most in real-world design and development workflows.

Evaluation Criteria

Output quality

Did the generated result look polished, realistic, and ready to build upon? I evaluated how professional the final output was.

Ease of use

Some AI tools produce useful results within minutes, while others require more setup or experience. I considered how quickly someone could go from opening the platform to generating a meaningful result.

Developer friendliness

For tools that generate code or applications, I paid attention to code quality, framework support, export options, maintainability, and how practical the generated project would be for continued development.

Design consistency

Whenever a tool generated multiple screens or visual assets, I checked whether typography, colors, spacing, components, and branding remained consistent across the entire project.

Pricing and value

Free plans, pricing structure, and overall value were also important. Some tools justify premium pricing through advanced capabilities, while others offer generous free tiers.

By evaluating every platform through these criteria, the comparison focuses on how these AI design tools perform during real projects.

Quick Comparison Table

Before exploring each tool in detail, this comparison table provides a quick overview of the 24 best AI design tools in 2026. It compares AI UI design tools, AI website builders, AI graphic design tools, AI image generators, AI coding tools, and AI UX tools based on their category, ideal use case, free availability, starting price, and standout capabilities to help designers and developers quickly find the right AI design software for their workflow.

Tool	Category	Best For	Free Plan	Starting Price	Standout Feature
Flowstep	AI UI Design & Code Export	Product designers & frontend teams	✅	$15/mo	Multi-screen UI generation + React export + MCP
Google Stitch	AI UI Design & Code Export	Design systems	✅	Free	Automatic design system generation
Figma Make	AI UI Design & Code Export	Figma users	✅	Included with Figma AI	Interactive apps inside Figma
Lovable	AI App Builder	Startup founders	✅	$25/mo	Full-stack app generation
Bolt.new	AI App Builder	Rapid prototyping	✅	$25/mo	Prompt-to-app workflow
Replit	AI App Builder	Developers	✅	$20/mo	Cloud IDE with AI
v0	AI App Builder	Frontend developers	✅	$30/mo	Production-ready React components
Framer	AI Website Builder	Marketing websites	✅	$10/mo	AI website publishing
Canva AI	Graphic Design	Marketing teams	✅	$6.50/mo	Magic Design
Adobe Firefly	Graphic Design	Creative professionals	✅	$9.99/mo	Commercial-safe AI generation
Microsoft Designer	Graphic Design	Beginners	✅	Free	Microsoft ecosystem integration
Kittl AI	Graphic Design	Branding & print	✅	$15/mo	Typography-first design
Playground AI	AI Image Generator	Creators	✅	$15/mo	Built-in image editing
Ideogram	AI Image Generator	Typography images	✅	$20/mo	Best AI text rendering
Leonardo AI	AI Image Generator	Professional concept art	✅	$12/mo	High-quality visual assets
Stable Diffusion	AI Image Generator	Advanced users	✅	Free / Credits	Fully customizable open-source models
Cursor	AI Coding Tool	Developers	✅	$20/mo	Codebase-aware AI agent
Cline	AI Coding Tool	Autonomous coding	✅	API cost	Multi-step coding agent
Devin Desktop	AI Coding Tool	Engineering teams	✅	$20/mo	Autonomous software engineer
GitHub Copilot	AI Coding Tool	Everyday coding	✅	$10/mo	AI pair programmer
Khroma	Design Workflow & UX	Color systems	✅	Free	Personalized palettes
Fontjoy	Design Workflow & UX	Typography	✅	Free	AI font pairing
Webflow	Design Workflow & UX	Website publishing	✅	$25/mo	Visual CMS & hosting
Miro AI	Design Workflow & UX	Product planning	✅	$10/mo	AI whiteboards

The 24 AI design tools below are grouped by category so you can compare platforms that solve similar problems. Instead of ranking completely different products against each other, this approach makes it easier to choose the right tool based on your workflow

AI UI Design & Code Export Tools

AI UI design tools have evolved far beyond generating attractive mockups. The best platforms can now create complete multi-screen experiences, maintain a consistent design system, export production-ready code, and integrate directly into modern development workflows.

For designers, this means spending less time rebuilding repetitive layouts and focusing more on refining user experiences. For developers, it shortens the journey from an idea to working code by generating reusable components and structured frontend foundations.

To make this comparison fair, I tested every tool in this category using the same prompt.

Prompt Used

Design a modern personal finance and budgeting platform called BudgetFlow.

Generate these four screens:

• Financial Dashboard
• Transactions
• Budgets
• Savings Goals

Requirements:

• Modern fintech UI
• Light theme with subtle gradients
• Charts and analytics
• Responsive layout
• Search and filtering
• Transaction categories
• Progress indicators
• Reusable design system
• Consistent navigation
• Accessible color contrast

1. Flowstep

Flowstep is one of the most advanced AI UI design tools for turning text prompts into production-ready interfaces. After using it, I noticed that it doesn't only focus on creating attractive screens; it tries to close the gap between design and development from the beginning.

The platform can generate multiple connected screens from a single prompt, accepts references such as images, URLs, or design documents, supports manual editing alongside AI generation, and exports clean React, TypeScript, and Tailwind CSS code. It also integrates with MCP-compatible AI coding tools, making it an appealing option for teams that want to move quickly from concept to implementation.

What it generated

For the BudgetFlow prompt, Flowstep generated a complete fintech interface that felt like a real product instead of a collection of unrelated screens. The dashboard, transactions page, budgets view, and savings goals all shared the same visual language, with consistent navigation, spacing, typography, and reusable components.

I also liked how well it organized financial data. Charts, transaction tables, budget cards, and progress indicators all looked natural, giving the interface a polished feel that looked ready to move into a design workflow.

Best for

UI/UX designers, product designers, startups, and development teams looking for a smooth transition from AI-generated designs to production-ready frontend code.

Key features

Multi-screen UI generation
AI and manual editing on the same canvas
React, TypeScript, and Tailwind CSS export
Copy directly into Figma
Reference-based generation
MCP integration
Auto Layout support
Collaborative editing

Pricing

Free plan available, with paid plans starting at $15/month with message-based pricing, collaboration features, and advanced capabilities.

2. Google Stitch

Google Stitch approaches AI UI generation differently from most competitors. Instead of only creating interfaces, it places a strong emphasis on building structured design systems alongside the generated screens.

Powered by Google's Gemini models, Stitch generates interfaces together with reusable colors, typography, spacing, and component styles. It also offers several export options that allow teams to continue working inside their preferred design or development tools.

What it generated

Google Stitch produced a clean and modern budgeting application that looked balanced. The four requested screens maintained strong consistency throughout the project, and the automatically generated design system made the output feel more structured.

The dashboards handled financial information well, with readable charts, organized transaction lists, and a clear visual hierarchy that made the interface clean.

Best for

Product designers, design teams, and organizations that prioritize design systems and structured UI foundations before moving into development.

Key features

Prompt-based UI generation
Automatic design system creation
Design tokens
Responsive layouts
Gemini-powered generation
HTML export
Export to Figma and other supported platforms
Reusable component generation

Pricing

Free to use through Google Labs with monthly generation limits.

3. Figma Make

Figma Make extends the familiar Figma workflow by introducing AI-powered application generation directly inside the platform many designers already use every day. It produces interactive experiences that can continue evolving through conversational prompts.

Because everything happens inside Figma, teams can move naturally between AI generation, collaborative editing, prototyping, and developer handoff without constantly switching between different applications.

What it generated

Figma Make generated a cohesive BudgetFlow interface with all four requested screens while maintaining strong consistency across typography, spacing, colors, and reusable UI components.

During testing the workflow was very comfortable. It wasn't treating AI as a separate tool; it was like a natural extension of the existing Figma experience. Being able to refine the generated interface through conversation while staying inside the same workspace made iteration enjoyable.

Best for

Design teams already working inside Figma who want to accelerate ideation, prototyping, and collaborative product design using AI.

Key features

AI-powered application generation
Native Figma workflow
Conversational refinement
Interactive prototypes
Editable design files
TypeScript export
Team collaboration
Design reasoning during generation

Pricing

Included with supported Figma AI features, with availability depending on your Figma plan.

AI App & Website Builders

AI app builders have changed what "prototyping" means.

A few years ago, turning an idea into a working application required designing every screen, building the frontend, connecting the backend, and wiring everything together before anyone could interact with the product. Today's AI app builders shorten that process. Many of them can generate complete applications from a single prompt, including navigation, authentication, responsive layouts, and editable source code.

That doesn't mean every platform serves the same audience. Some prioritize developer control, while others focus on helping founders validate ideas quickly or enabling designers to build functional products without writing much code.

To compare them fairly, I gave every tool in this category the same prompt and evaluated how well it handled the overall application, the user experience, code quality, navigation, and developer workflow.

Prompt Used

Build a complete event management web application called EventSphere.

Include:

• Landing Page
• Event Discovery
• Ticket Checkout
• User Dashboard

Requirements:

• Working navigation
• Interactive booking flow
• Responsive layout
• Authentication
• Modern UI
• Dummy event data
• Search and filters
• Functional components

4. Lovable

Lovable has quickly become one of the most popular AI app builders because it focuses on something many developers and founders care about: turning an idea into a working product with as little friction as possible. It creates complete applications that can continue evolving through conversation.

Its biggest strength is how it combines interface generation, application logic, and iterative development into a single workflow. Instead of starting from a blank project, you begin with a functional application that you can refine as you want.

What it generated

Lovable produced a complete EventSphere application with all four requested pages connected through a coherent user flow. The landing page introduced upcoming events, the discovery page supported searching and filtering, the checkout process was structured, and the user dashboard provided a realistic overview of purchased tickets and upcoming events.

What impressed me most was that the application was complete from the first generation. Instead of producing disconnected pages, it generated a product that you could already continue building into an MVP.

Best for

Startup founders, product teams, developers, and anyone who wants to validate ideas by generating functional web applications quickly.

Key features

Full-stack application generation
Natural language development
Interactive editing through chat
Authentication support
Backend integration
Responsive layouts
Rapid MVP creation
One-click deployment options

Pricing

Free plan available, with paid plans offering additional usage, faster generation, and advanced project capabilities.

5. Bolt.new

Bolt.new is between an AI coding assistant and an AI software engineer. It doesn't stop after generating interface mockups; it creates a working application that developers can inspect, modify, and extend immediately.

Bolt has become popular thanks to its conversational workflow. Every iteration feels like collaborating with another developer.

What it generated

Bolt generated the complete EventSphere application with all four requested pages while maintaining a consistent visual identity throughout the project. Navigation between pages worked well, and the generated interface was clean and functional.

Compared with some other tools, the visuals leaned toward simplicity rather than elaborate design, but the generated application provided a solid technical foundation that developers could continue building.

Best for

Developers looking for fast AI-assisted web application development and iterative product building.

Key features

Prompt-to-app generation
Live development environment
React project generation
Conversational editing
Source code export
Deployment integrations
Rapid application iteration
Modern frontend workflows

Pricing

Free plan available with usage limits, alongside paid plans for higher limits and premium features.

6. Replit

Replit approaches AI-generated applications from a developer-first perspective. It focuses on generating projects that can immediately be executed, edited, tested, and deployed inside its cloud-based development environment.

For developers who prefer working directly with source code while still benefiting from AI-assisted generation, that workflow is a good choice for them.

What it generated

Replit generated a fully functional EventSphere application with all requested pages connected through working navigation. The generated project included realistic event listings, structured booking flows, responsive layouts, and editable source code that could immediately be expanded.

During testing I really enjoyed the visual quality of the generated interface. Replit produced a polished layout with well-balanced colors, clear typography, and a clean overall structure.

Best for

Developers who want AI-generated applications inside a complete cloud development environment.

Key features

AI full-stack generation
Browser-based IDE
Live preview
Integrated deployment
AI code editing
Collaborative development
Source code access
Cloud hosting

Pricing

Free plan available, with paid subscriptions unlocking more AI usage, private projects, and advanced development features.

7. v0 by Vercel

Although v0 is often associated with frontend component generation, it has evolved into a capable AI application builder for modern React ecosystems. Instead of producing static designs, it generates structured React interfaces that fit into existing frontend projects.

Developers working with React, Next.js, Tailwind CSS, and modern component libraries will likely find its workflow immediately familiar.

What it generated

v0 generated a modern EventSphere interface with clean page layouts, reusable components, responsive navigation, and structured frontend architecture.

The generated project emphasized component quality over visual experimentation. The resulting interface looked easy to extend and aligned with current frontend development practices.

Best for

Frontend developers building React and Next.js applications who want production-ready AI-generated components.

Key features

React generation
TypeScript support
Tailwind CSS integration
Component architecture
Responsive layouts
Chat-based refinement
Modern frontend patterns
Easy project integration

Pricing

Free plan available, with Pro plans offering additional generations, higher limits, and premium AI capabilities.

8. Framer

Framer has evolved from a prototyping platform into one of the strongest AI website builders available today. Instead of generating wireframes, it creates complete responsive websites that can be published with minimal additional work.

Its AI generation feels polished for marketing websites, landing pages, and product-focused experiences where visual presentation is just as important as functionality.

What it generated

Framer generated a polished EventSphere website with an attractive landing page, event listings, booking sections, and a user dashboard that maintained consistent styling throughout the experience.

The layouts looked modern, spacing was balanced, and the generated website required little cleanup. While it isn't intended to replace full-stack development platforms, it performed extremely well for creating production-quality marketing experiences.

Best for

Designers, marketers, agencies, and startups building modern websites and landing pages without starting from scratch.

Key features

AI website generation
Responsive layouts
Interactive prototypes
Built-in CMS
Hosting
SEO optimization
Visual editing
Publishing workflow

Pricing

Free plan available, with paid plans unlocking custom domains, CMS features, advanced publishing, and team collaboration.

AI Graphic Design & Visual Content Tools

AI graphic design tools help designers, marketers, and content creators produce polished visual assets much faster than traditional design workflows. These tools specialize in creating marketing materials, social media graphics, presentations, branding assets, and commercial-ready visuals that can be customized for different campaigns.

As these AI design tools continue to improve, they are becoming valuable assistants. They accelerate repetitive tasks, generate creative concepts, and help teams maintain consistent branding across multiple channels without stealing human creativity and refinement.

To compare these tools fairly, I used the same branding challenge for every platform and evaluated how well each one handled visual consistency, typography, layout quality, brand identity, and overall presentation.

Prompt Used

Create a LinkedIn banner for a sustainable travel company called EcoVoyage.

Requirements:

• Premium branding
• Nature-inspired colors
• Consistent typography
• Modern layouts
• Commercial-ready visuals
• Cohesive brand identity

9. Canva AI

Canva AI has moved from a beginner-friendly design platform into one of the best AI graphic design tools available today. Its AI features help users generate layouts, edit images, write marketing copy, and build complete visual campaigns while keeping everything inside Canva's familiar drag-and-drop editor.

One of Canva AI's biggest strengths is accessibility. Designers, marketers, entrepreneurs, and content creators can move from an idea to a polished design without switching between multiple applications. Combined with thousands of templates and brand management features, it has become a practical solution for everyday design work.

What it generated

For the EcoVoyage branding challenge, Canva AI produced 4 professional LinkedIn banners featuring a clean layout, balanced typography, and nature-inspired visuals. The overall design communicated the sustainable travel theme.

The typography hierarchy, spacing, and color palette were solid, but the generated banner required only minor adjustments before being presentation-ready. It needs to be resized because it doesn't have the best ratio for a LinkedIn banner.

Best for

Marketing teams, content creators, small businesses, freelancers, and designers creating social media graphics and branded marketing assets.

Key Features

AI-powered design generation
Magic Design
Brand Kit integration
AI image editing
Large template library
Social media content creation

Pricing

Free plan available. Canva Pro offers additional generations and higher limits.

10. Adobe Firefly

Adobe Firefly is Adobe's generative AI platform built specifically for creative professionals. Unlike many AI image generators, Firefly focuses on commercially safe content and integrates directly into Adobe Creative Cloud applications such as Photoshop, Illustrator, and Express.

Adobe Firefly is not here to replace professional design workflows; it enhances them by helping designers generate images, backgrounds, text effects, and creative assets while maintaining compatibility with industry-standard design tools.

What it generated

For the EcoVoyage campaign, Adobe Firefly generated a polished LinkedIn banner with premium-looking visuals, harmonious color choices, and a refined layout. The generated imagery reflected the sustainable travel concept while maintaining a realistic and professional appearance.

The overall design was well suited for commercial branding projects. The image quality, typography placement, and visual balance are good, making Firefly a strong choice for teams already working within the Adobe ecosystem.

Best for

Professional designers, creative agencies, enterprise marketing teams, and businesses already using Adobe Creative Cloud.

Key Features

Commercially safe AI generation
Photoshop integration
Illustrator integration
Generative Fill
AI text effects
Brand-safe creative workflows

Pricing

Free plan with limited AI credits. Premium features are included with eligible Adobe Creative Cloud subscriptions.

11. Microsoft Designer

Microsoft Designer combines AI-powered graphic design with Microsoft's productivity ecosystem, making it easy to create banners, presentations, advertisements, and social media graphics without requiring advanced design skills. Powered by Microsoft's AI technologies, it emphasizes simplicity and speed.

For users already working with Microsoft 365, Designer fits naturally into existing workflows by allowing quick creation of branded visuals that can be shared across presentations, documents, and marketing campaigns.

What it generated

For the EcoVoyage branding task, Microsoft Designer produced 4 modern LinkedIn banners featuring clean layouts, attractive nature-themed imagery, and balanced typography. The overall design captured the requested branding direction while keeping the layout simple and easy to customize.

The banner size was the required size for a LinkedIn banner, and the typography hierarchy, spacing, and color palette were cohesive, making it one of the fastest tools for producing marketing graphics.

Best for

Microsoft 365 users, marketers, entrepreneurs, educators, and businesses creating everyday marketing materials.

Key Features

AI graphic design generation
Microsoft 365 integration
Text-to-design
AI image generation
Brand templates
Social media design tools

Pricing

Free plan available with a Microsoft account. Premium capabilities are included with select Microsoft 365 subscriptions.

12. Kittl AI

Kittl AI focuses on high-quality graphic design with a strong emphasis on branding, typography, print design, and marketing assets. Unlike general-purpose AI design platforms, Kittl combines AI generation with powerful editing tools that allow designers to refine every visual element before exporting the final design.

It excels at producing polished branding materials, promotional graphics, merchandise designs, posters, and social media assets while giving users more creative control.

What it generated

For the EcoVoyage branding campaign, Kittl AI created a premium LinkedIn banner with elegant typography, sophisticated composition, and a solid visual identity centered around sustainable travel. The design was polished and crafted.

The combination of modern layouts, carefully selected colors, and strong typography made it one of the most visually refined outputs in this category. But it needs to be resized because it doesn't have the best ratio for a LinkedIn banner.

Best for

Brand designers, marketing professionals, creative agencies, print designers, and businesses building premium visual identities.

Key Features

AI-powered graphic design
Advanced typography tools
Brand asset creation
Poster and social media design
Illustration generation
Professional editing controls

Pricing

Free plan available. Paid plans unlock premium templates, higher export quality, and advanced AI features.

AI Image Generation Tools

AI image generators solve a different problem compared with AI UI design tools, AI website builders, or AI coding tools. Instead of creating complete interfaces or functional applications, they focus on transforming text descriptions into original visual assets.

These tools have become essential for designers, marketers, and creative teams who need illustrations, concept art, product visuals, marketing graphics, and brand assets without starting every project from a blank canvas. Modern AI image generators can now understand complex visual instructions, maintain consistent styles, and produce professional-quality images suitable for real-world projects.

For this category, I tested each tool using the same prompt to compare how different AI models interpret the same creative direction, including image quality, composition, lighting, realism, and overall consistency.

Prompt Used

Create a cinematic illustration of a futuristic software engineer working inside a neon-lit workspace.

Include:

• multiple monitors
• holographic UI
• realistic lighting
• reflections
• depth of field
• subtle cyberpunk atmosphere

Style:

• Highly detailed
• Professional concept art
• Commercial quality
• 4K

13. Playground AI

Playground AI is an accessible AI image generator designed for creators who want to generate and refine visuals without dealing with complex workflows. It combines text-to-image generation with editing capabilities, allowing users to create concepts, modify existing images, and experiment with different styles from one workspace.

Compared with purely prompt-based AI image tools, Playground AI focuses more on creative exploration. It gives designers and content creators an easier way to iterate on ideas, making it useful for social media graphics, marketing visuals, illustrations, and early-stage design concepts.

What it generated

For this test, Playground AI generated a futuristic software engineer workspace with the requested cyberpunk atmosphere, multiple monitors, and glowing holographic interfaces. The overall composition followed the prompt well, with strong lighting effects.

The final result captured the general mood of professional concept art, and the image quality was strong enough for creative exploration and visual inspiration.

Best for

Designers, marketers, content creators, and professionals looking for an easy-to-use AI image generator with built-in editing features.

Key Features

Text-to-image generation
AI image editing and variations
Canvas-based creative workflow
Multiple AI image models
Image enhancement tools
Fast visual experimentation

Pricing

Free plan available. Paid plans provide higher generation limits, faster processing, and access to additional features.

14. Ideogram

Ideogram is one of the most popular AI image generators for creating visuals where typography and text accuracy matter. While many AI image tools struggle with generating readable text inside images, Ideogram is a strong option for posters, advertisements, logos, branding concepts, and social media graphics.

The platform focuses on combining creative image generation with better text understanding. This makes it useful for designers who need AI-generated visuals that include headlines, product names, or branded elements.

What it generated

For the futuristic software engineer prompt, Ideogram produced 4 highly polished concept illustrations with strong composition and attractive lighting. The generated environment included detailed screens, futuristic interfaces, and a cinematic atmosphere that closely matched the requested direction.

The result was closer to professional concept art. The lighting, depth of field, and overall visual balance were among the strongest aspects.

Best for

Brand designers, marketing teams, creative agencies, and professionals creating posters, advertisements, and visual campaigns.

Key Features

Advanced text rendering in AI images
High-quality image generation
Multiple artistic styles
Prompt-based image refinement
Branding and marketing visual creation
Image variations and editing

Pricing

Free plan available with limited generations. Paid plans increase generation limits, processing speed, and access to premium features.

15. Leonardo AI

Leonardo AI is a powerful AI image generation platform focused on producing detailed, production-ready visuals. It is widely used by designers, creative teams, game developers, and marketers who need more control over image styles, characters, and visual consistency.

Unlike simpler AI image generators, Leonardo AI provides more customization options through different models, image controls, and editing features. This makes it suitable for users who want to move beyond basic prompt generation and create assets for professional projects.

What it generated

Leonardo AI generated one of the most detailed images in this comparison. The futuristic workspace included realistic lighting, reflections, multiple displays, and a strong sense of depth that matched the requirements of the prompt.

The final output looked close to professional concept art, with impressive environmental details and cinematic quality. The tool handled the futuristic atmosphere very well, creating a scene that could work for marketing visuals, presentations, or creative inspiration.

Best for

Professional designers, creative agencies, game developers, and teams creating high-quality visual assets.

Key Features

High-quality AI image generation
Custom AI models
Image editing and refinement
Style customization
Asset creation workflows
Advanced generation controls

Pricing

Free plan available with daily credits. Paid plans provide additional generations, higher limits, and advanced features.

16. Stable Diffusion

Stable Diffusion stands apart from many AI image generators because it is an open-source AI model. It gives developers and advanced creators the ability to run models locally, customize workflows, fine-tune outputs, and build highly specialized image-generation systems.

This flexibility has made Stable Diffusion popular among technical users who want maximum control over AI-generated visuals. Stable Diffusion prioritizes customization, making it a powerful option for users comfortable with more advanced workflows.

What it generated

For this experiment, Stable Diffusion generated 2 images, and it successfully captured the requested futuristic software engineer concept, including the neon workspace, holographic interfaces, multiple monitors, and cinematic lighting.

The quality of the final image depended heavily on model selection and prompt adjustments. With the right configuration, Stable Diffusion can produce extremely detailed results, but achieving the best output usually requires more experimentation compared with beginner-friendly AI image generators.

Best for

Advanced designers, AI enthusiasts, developers, and creative professionals who want maximum control over AI-generated images.

Key Features

Open-source AI image models
Local deployment options
Custom model support
Fine-tuning capabilities
Advanced image control
Community-created workflows

Pricing

Free when self-hosted. Cloud-based Stable Diffusion services usually use credit-based pricing models.

AI Coding Agents & Developer Tools

Modern AI coding agents can understand entire codebases, generate components, explain implementation decisions, refactor complex logic, and help developers move from ideas to working software faster.

Unlike traditional coding assistants that only suggest individual lines of code, AI tools for developers now work as collaborative engineering partners. They can analyze project context, modify multiple files, debug issues, and assist with complete development workflows.

For this category, I focused on a real frontend development case. I tested how each AI coding tool handles component architecture, code quality, accessibility requirements, and its ability to explain technical decisions.

Every tool received the same development challenge.

Prompt Used

Build a reusable React component library for a healthcare appointment platform.

Include:

• Appointment Card
• Doctor Profile Card
• Calendar Component
• Search Component
• Notification Toast
• Loading Skeleton

Requirements:

• TypeScript
• Tailwind CSS
• Accessibility (WCAG)
• Dark mode
• Responsive design
• Clean architecture
• Reusable components
• Explain implementation decisions

17. Cursor

Cursor has become one of the most popular AI coding tools because it combines a familiar code editor experience with powerful AI agents. Built on top of the VS Code ecosystem, it understands project context and helps developers generate features, refactor code, debug problems, and navigate large applications.

Cursor focuses on understanding the relationship between files, components, and application architecture. This makes it valuable for frontend developers working on React applications, design systems, and production codebases.

What it generated

For this healthcare component library task, Cursor generated a structured React project with reusable components, TypeScript types, and Tailwind CSS styling. The generated components followed common frontend patterns, making the code easier to maintain and extend.

One of the strongest parts of the output was the explanation of implementation decisions. Cursor did not only generate code but also explained choices around component structure, accessibility considerations, and reusability, which makes it useful for developers learning from AI-generated solutions.

Best for

Frontend developers, full-stack developers, software engineers, and teams building modern web applications.

Key Features

AI coding agent with project understanding
Multi-file code generation and editing
Code explanation and debugging
Natural language development workflow
VS Code-compatible environment
Context-aware suggestions

Pricing

Free plan available. Paid plans provide higher usage limits, larger context windows, and additional AI capabilities.

18. Cline

Cline is an open-source AI coding agent designed for developers who want a more autonomous development workflow. It can analyze tasks, create implementation plans, edit files, run commands, and iterate through development steps.

Its biggest difference is the agent-based approach. Developers can provide a high-level goal, and Cline attempts to complete multiple stages of the task while keeping track of the project context.

What it generated

For the React component library challenge, Cline created a clean component structure with reusable UI elements and logical file organization. It handled the TypeScript and Tailwind CSS requirements effectively while maintaining consistency between different components.

The generated solution also demonstrated strong awareness of accessibility requirements. Components included reusable patterns that could be adapted for production applications, although developers would still need to review and refine the implementation before shipping.

Best for

Developers working on larger applications who want an autonomous AI coding assistant capable of handling multi-step engineering tasks.

Key Features

Autonomous coding agent
Project-wide code understanding
File creation and modification
Terminal command execution
Multi-step development workflows
Open-source architecture

Pricing

Open-source and free to use. Costs depend on the AI model connected to Cline.

19. Devin Desktop

Devin Desktop represents a more advanced direction for AI coding tools: an AI software engineer capable of handling longer development tasks with less continuous guidance. It aims to manage complete engineering workflows from planning to implementation.

The platform is designed around the idea of AI agents working alongside developers. It can analyze requirements, write code, review changes, and help automate repetitive software development activities.

What it generated

For this test, Devin Desktop generated a complete React component library structure with reusable healthcare UI components. The output included TypeScript definitions, component organization, and styling approaches aligned with modern frontend development practices.

The generated implementation explanations were useful because they provided context behind architectural decisions. While AI-generated code still requires developer review, Devin Desktop showed how AI agents can support larger software engineering workflows.

Best for

Engineering teams automating complex development tasks and organizations exploring AI-assisted software engineering workflows.

Key Features

Autonomous software engineering
Long-running development tasks
Code generation and modification
Project analysis
AI-assisted development workflows
Engineering task automation

Pricing

Free plan available. Paid plans provide higher usage limits.

20. GitHub Copilot

GitHub Copilot remains one of the most widely adopted AI coding assistants because it integrates directly into the workflows developers already use. It enhances traditional development practices by providing code suggestions, explanations, debugging assistance, and productivity improvements.

As part of the GitHub ecosystem, Copilot benefits from deep integration with popular development environments. It is particularly useful for developers who want AI assistance while maintaining full control over implementation decisions.

What it generated

For the healthcare component library challenge, GitHub Copilot generated clean React components using TypeScript and Tailwind CSS. But the output was very basic, and the loading skeleton was almost empty, but it could be a good starting point for building reusable UI elements.

Compared with more autonomous AI coding agents, Copilot required more developer direction during implementation.

Best for

Frontend developers, software engineers, and teams already using Visual Studio Code or GitHub-based workflows.

Key Features

AI code completion
Coding chat assistant
Code explanations
Debugging support
Multi-language programming support
IDE integration

Pricing

Free plan available. Paid plans provide additional AI features, higher usage limits, and advanced capabilities.

Design Workflow & UX Tools

Not every AI design tool is built to generate interfaces, write code, or create visual assets. Some tools focus on improving the overall design workflow by helping teams understand users, build stronger design systems, organize ideas, and make better product decisions.

These AI UX tools support different stages of the product design process, from user research and usability testing to typography selection, color exploration, website creation, and team collaboration. They are especially valuable for product designers and teams that want to combine AI automation with a more structured design workflow.

Because each tool in this category solves a different problem, I did not use the same prompt. Instead, I created a task that matches the main purpose of each tool and evaluated how effectively it supports real design workflows.

21. Khroma

Khroma is an AI color palette generator designed to help designers discover color combinations based on their preferences and project requirements. Instead of manually searching through thousands of color combinations, designers can use AI recommendations to quickly explore suitable palettes.

The tool is useful during branding and UI design stages, where choosing the right colors can significantly impact usability, accessibility, and product identity.

Prompt Used

Generate a modern color palette for Aurora Learning Platform.

The brand should feel:

• Professional
• Trustworthy
• Educational
• Modern
• Accessible

What it generated

For the Aurora Learning Platform branding task, Khroma generated several color combinations that matched the requested direction. The suggested palettes balanced professional and educational qualities with a modern visual style.

The generated options were useful as a starting point for building a design system. While designers would still need to validate colors against accessibility standards and brand guidelines, Khroma helped reduce the time spent exploring initial color directions.

Best for

UI designers, brand designers, product teams, and anyone building visual design systems.

Key Features

AI-generated color palettes
Personalized color recommendations
Brand inspiration
Color combination exploration
HEX and RGB values
Visual palette previews

Pricing

Free.

22. Fontjoy

Fontjoy is an AI typography tool that helps designers discover font combinations that work well together. Typography plays an important role in user experience, especially for digital products where readability and visual hierarchy directly affect how users consume information.

Instead of manually testing hundreds of font combinations, Fontjoy uses machine learning to suggest pairings based on contrast, similarity, and overall design balance.

Prompt Used

Generate font pairings for Aurora Learning Platform.

Requirements:

• Readable
• Modern
• Accessible
• Suitable for dashboards
• Suitable for marketing pages

What it generated

For the Aurora Learning Platform typography task, Fontjoy suggested several font combinations that balanced modern aesthetics with readability. The generated pairings were suitable for both marketing pages and dashboard interfaces.

The results provided a useful starting point for defining a product’s typography system. While final font choices would depend on brand identity and accessibility testing, Fontjoy made the exploration process faster and more structured.

Best for

UI designers, brand designers, frontend developers, and marketing teams creating digital products.

Key Features

AI font pairing recommendations
Typography exploration
Font compatibility analysis
Design inspiration
Readability-focused suggestions

Pricing

Free.

23. Webflow

Webflow combines visual website building with powerful publishing capabilities, allowing designers to create responsive websites without writing every line of code. It is between traditional website builders and professional development workflows, giving designers visual control while producing websites that are ready to publish.

For designers, freelancers, and marketing teams, Webflow is useful for creating landing pages, marketing websites, and interactive experiences where visual quality and customization matter.

Prompt Used

Build the marketing landing page for Aurora Learning Platform.

Include:

• Hero
• Features
• Testimonials
• Pricing
• CTA
• Responsive layout

What it generated

For the Aurora Learning Platform landing page task, Webflow generated a complete website structure containing the main sections requested: hero area, feature highlights, testimonials, pricing, and calls to action.

The generated layout followed common SaaS website patterns with responsive design considerations. While further customization would be needed for a final production website, the output provided a strong foundation for quickly moving from idea to a functional marketing page.

Best for

Web designers, freelancers, agencies, startups, and marketing teams building professional websites.

Key Features

Visual website builder
Responsive design controls
CMS capabilities
SEO tools
Website publishing
Custom interactions and animations

Pricing

Free workspace available. Paid plans are required for advanced website hosting and additional features.

24. Miro AI

Miro AI focuses on collaboration, brainstorming, and product planning. It brings AI assistance into digital whiteboards, helping teams organize ideas, document workflows, and structure product discussions.

For UX teams and product managers, Miro AI can accelerate early-stage planning by turning simple descriptions into structured boards that teams can refine collaboratively.

Prompt Used

Generate a product planning board for Aurora Learning Platform.

Include:

• User personas
• User journey
• Feature prioritization
• Roadmap
• Brainstorm ideas

What it generated

For the product planning task, Miro AI created a structured workspace containing user personas, journey mapping, feature prioritization ideas, roadmap sections, and brainstorming notes.

The generated board was useful as a starting point for product workshops and team discussions. Miro AI works as a productivity layer that helps teams organize information faster and collaborate more effectively.

Best for

Product managers, UX teams, design teams, and organizations running collaborative planning sessions.

Key Features

AI brainstorming assistance
Product planning boards
User journey mapping
Collaborative whiteboards
Workshop organization
Team collaboration tools

Pricing

Free plan available. Paid plans provide additional collaboration features, larger boards, and enterprise capabilities.

Which AI Design Tools Should You Choose?

After testing all 24 AI design tools, I can say that there isn't a single "best" AI design tool. Every platform is designed to solve a different problem across the design and development workflow.

Some tools help you explore ideas faster. Others generate production-ready code. Some are built for branding and visual content, and others focus on UX research or developer productivity.

A better question is:

Which AI design tool solves the problem you're trying to solve today?

Here's where I think each category shines.

If you're designing UI for web or mobile applications

If your workflow starts with product interfaces, AI UI design tools will save you the most time.

Flowstep impressed me the most for complete product flows because it combines multi-screen generation with editable designs, React code export, and developer-friendly workflows.

If your goal is building complete applications

Sometimes you don't need mockups; you need a working product.

That's where AI app builders become much more valuable than traditional design tools.

Lovable goes beyond interface generation by producing functional applications that developers can continue building immediately.

This tool significantly reduces the distance between an idea and a working prototype.

If you're creating marketing assets or brand content

Design teams don't only build software.

They also create banners, advertisements, presentations, landing pages, and social media graphics.

Canva AI remains one of the easiest AI graphic design tools for creating banners, presentations, social media graphics, and other marketing assets in minutes.

If you need high-quality AI-generated images

For concept art, blog illustrations, product visuals, presentations, and creative assets, dedicated AI image generators still produce the best results.

Leonardo AI produced some of the most detailed and realistic images during my testing, making it an excellent choice for concept art and professional creative work.

If you're a frontend developer

Developer workflows have changed over the past year.

Instead of writing every component manually, AI coding agents can now generate reusable React components, explain implementation decisions, refactor projects, and even understand entire codebases.

Cursor stood out because it understands entire codebases instead of isolated files, making it feel like working with another experienced developer.

If your work focuses on UX and product strategy

Not every design decision starts with pixels.

Sometimes you need better research, better planning, or stronger design systems.

Khroma helps build stronger color systems, Fontjoy simplifies typography selection, Webflow speeds up website creation, and Miro AI makes brainstorming and product planning more organized.

These tools don't replace designers; they help designers make better decisions.

Frequently Asked Questions

What are the best AI design tools in 2026?

→ The best AI design tools depend on what you're building. For UI and product design, Flowstep is an excellent choice. For AI app development, Lovable stands out. Canva AI is a strong option for graphic design, while Cursor is one of the best AI coding tools for developers.

Which AI design tool is best for UI/UX designers?

→ If your work focuses on digital products, Flowstep, Google Stitch, and Figma Make are among the best AI UI design tools available today. They can generate complete interfaces, maintain consistent design systems, and integrate smoothly with modern development workflows.

Can AI design tools generate production-ready code?

→ Yes, but not all of them.

Some AI design tools focus only on interface generation, while others produce reusable code alongside the design.

Platforms like Flowstep, v0 by Vercel, Replit, Bolt.new, and Lovable can generate code that developers can continue building on. AI coding tools such as Cursor, Cline, Devin Desktop, and GitHub Copilot further accelerate implementation by generating, refactoring, and explaining code.

Should designers use multiple AI tools instead of just one?

→ In most cases, yes.

No single AI design tool excels at every task. Many designers combine several specialized tools depending on the project. For example, you might use Flowstep for interface design, Leonardo AI for illustrations, Canva AI for marketing assets, and Cursor to accelerate frontend development. Building a small AI toolkit usually produces better results than relying on one platform for everything.

Final Thoughts

When I started testing these AI design tools, I expected to compare interface quality. But I ended up comparing completely different ways of building digital products.

Some platforms are built with designers in mind, while others focus on helping developers ship code faster. Some specialize in generating polished interfaces, whereas others create complete applications or improve research, branding, and collaboration.

That's what makes today's AI design ecosystem so interesting.

These tools aren't replacing designers or developers; they're changing how they work. Designers can move from an idea to a polished interface faster. Developers spend less time writing repetitive blocks. Product teams validate concepts earlier, iterate more often, and collaborate more efficiently.

The most productive teams in 2026 won't be the ones using the most AI tools. They'll be the ones that know exactly when to use each one.

The takeaway from testing all 24 platforms is:

AI design tools are not experimental productivity boosters. They've become practical parts of modern product development.

And for every step of your work process, there's now an AI tool that can help you move faster without sacrificing quality.

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

What Is Agentic Marketing? How AI Agents Are Replacing the Modern Marketing Stack

Hadil Ben Abdallah — Tue, 28 Jul 2026 08:58:34 +0000

According to Gartner, by 2026, 80% of enterprise marketing organizations are expected to use generative AI in some capacity, highlighting how quickly AI is becoming part of modern marketing operations.

But there's a bigger shift happening behind the scenes. Marketing teams are moving beyond AI assistants that simply generate copy or analyze reports. Instead, they're adopting AI agents that can observe signals, make decisions, execute campaigns, and improve without waiting for human instructions.

This new operating model is called agentic marketing, and it's quickly becoming one of the most important changes in growth engineering.

In this guide, you'll learn exactly what agentic marketing is, how it differs from traditional marketing automation, why it's becoming practical now, and why platforms like Hellyeah AI represent an entirely new category of marketing infrastructure.

What Is Agentic Marketing?

Agentic marketing (also called autonomous marketing automation) is an AI-driven operating model where intelligent marketing agents continuously monitor behavioral data, campaign performance, customer lifecycle events, and business signals, then decide what action to take, execute that action, and learn from the outcome.

Unlike traditional marketing automation, which follows predefined rules created by humans, agentic marketing systems adapt their decisions based on real-time context without requiring someone to initiate every workflow or campaign.

Every agentic marketing system is built around three core capabilities:

Perception: constantly reading real-time signals from marketing channels, products, and customer behavior.
Decision: determining the best action based on current context instead of predefined rules.
Execution + Learning: performing the action, measuring the outcome, and improving future decisions automatically.

This is the fundamental difference between AI assistants and AI agents.

AI assistants help marketers perform tasks.

AI agents operate parts of the marketing function themselves.

Traditional Marketing Automation vs. Agentic Marketing

Dimension	Traditional Marketing Automation	Agentic Marketing
Operating model	Rule-based workflows configured by humans	Decision-based autonomous agents
When it acts	Fixed schedules or predefined triggers	Real-time based on live signals and context
Who initiates actions	Human marketers	AI agents
Adaptability	Static until someone edits workflows	Constantly adapts from new outcomes
Scale	Limited by workflow maintenance	Handles thousands of simultaneous decisions
Learning	Executes rules but doesn't improve itself	Learns from every interaction
Human role	Builds and manages workflows	Sets strategy while agents handle execution
Example	Send an email seven days after signup	Detect behavioral changes instantly and launch the best intervention automatically

The move from the left column to the right isn't just another upgrade to marketing automation.

It's a completely different way of running growth. Instead of asking marketers to configure thousands of workflows, agentic marketing lets AI agents monitor what's happening, determine the best next step, and execute it without waiting for manual intervention.

Why 2026 Is the Inflection Point for Agentic Marketing

Agentic marketing didn't suddenly appear because someone coined a new buzzword. It became practical because several technologies matured at the same time.

The first change was the rapid evolution of large language models. Between 2024 and 2025, AI models became capable of reasoning through multi-step problems, using external tools, remembering context, and making reliable decisions across complex workflows. Those capabilities transformed AI from something that generated content into something that could operate systems.

At the same time, the infrastructure finally caught up. Frameworks for AI agents became significantly easier to deploy, while purpose-built platforms like Hellyeah emerged specifically for marketing rather than adapting generic AI frameworks to marketing use cases. That reduced the engineering effort required to build autonomous marketing operations.

The economics of marketing also changed.

Many growth teams are now expected to manage more acquisition channels and customer touchpoints without significantly increasing headcount. Manual operations simply don't scale at the same rate as modern growth expectations.

Agentic marketing solves that imbalance by increasing execution capacity without forcing marketers to spend their days adjusting bids, launching experiments, reviewing dashboards, or manually moving data between disconnected tools.

The final reason is competitive pressure. Early industry case studies and vendor benchmarks suggest autonomous experimentation can significantly shorten campaign optimization cycles, although results vary depending on implementation maturity.

The conversation is not about whether AI agents will become part of marketing.

It's about how quickly organizations can redesign their marketing operations around them.

The Four Layers of an Agentic Marketing System

Agentic marketing isn't powered by one AI model doing everything. Instead, it operates through several specialized layers that constantly feed information into one another.

Layer	Function	What the Agent Does	Hellyeah Platform
Signal Layer	Reads data across the growth stack	Regularly monitors behavioral events, campaign metrics, customer lifecycle events, competitive signals, and product usage	Mutation, AIMA, Deja Vu, Forge
Decision Layer	Determines the optimal action	Evaluates current context, historical performance, business goals, and customer behavior before selecting the next action	Intelligence shared across all Hellyeah platforms
Execution Layer	Takes action automatically	Launches campaigns, reallocates budgets, sends personalized messages, starts workflows, rotates creatives, and triggers experiments	AIMA, Mutation, Forge, Deja Vu
Learning Layer	Constantly improves future decisions	Measures outcomes, updates optimization strategies, and feeds new insights back into the decision engine	Deja Vu, Mutation, AIMA

Notice how each layer depends on the others.

Without real-time signals, an AI agent makes poor decisions.

Without autonomous execution, even perfect decisions remain ideas.

Without continuous learning, performance eventually plateaus because yesterday's winning strategy becomes tomorrow's outdated one.

This closed feedback loop is what separates agentic marketing from traditional automation.

Hellyeah AI is built around this complete architecture. Rather than offering isolated automation features, it connects signal collection, decision-making, execution, and continuous learning into a shared operating system where each layer reinforces the others over time.

Instead of treating every campaign or workflow as an isolated task, Hellyeah creates a continuous feedback loop.

Every campaign outcome, behavioral response, experiment result, and conversion signal feeds back into the shared data layer. Those insights refine future budget allocation, personalization decisions, experimentation priorities, and workflow execution automatically, allowing every new decision to benefit from what the system has already learned.

Agentic Marketing Platforms Compared

While several AI-powered marketing platforms now automate parts of the marketing workflow, they focus on different layers of the agentic marketing stack. Here's how the major platforms compare.

Platform	Primary Focus	Best For	Pricing	Limitation
Hellyeah AI	Complete agentic marketing platform (signal, decision, execution, learning)	Organizations building an autonomous marketing operation	Enterprise	Requires clean event instrumentation and onboarding before the autonomous workflows deliver full value.
Jasper AI	AI content creation	Marketing teams producing blogs, ads, emails, and social content	Paid	Primarily focuses on content generation and doesn't provide autonomous campaign execution or behavioral decision-making.
HubSpot AI	CRM and marketing automation with AI assistance	Businesses already using the HubSpot ecosystem	Paid / Enterprise	AI capabilities mainly enhance existing HubSpot workflows rather than operating as autonomous marketing agents.
Adobe Sensei	Enterprise creative optimization and personalization	Large enterprises using Adobe Experience Cloud	Enterprise	Best suited for organizations already invested in Adobe's ecosystem and requires significant implementation.
Salesforce Einstein	AI-powered CRM insights and sales/marketing intelligence	Enterprise sales and marketing organizations	Enterprise	Strong customer intelligence but relies heavily on Salesforce infrastructure and customization.
Adobe Marketo Engage	Enterprise marketing automation	Large B2B marketing teams running complex nurture campaigns	Enterprise	Primarily rule-based automation with AI enhancements rather than a fully agentic operating model.

Most of these platforms excel at one layer of the marketing stack, whether that's content generation, CRM intelligence, or marketing automation. Hellyeah approaches the problem differently by combining signal detection, autonomous decision-making, execution, and continuous learning into a single agentic marketing system instead of treating them as separate products.

Hellyeah: The Purpose-Built Platform for Agentic Marketing

Everything we've covered so far leads to this conclusion: agentic marketing requires more than a collection of AI-powered tools. It needs a unified system where AI agents can perceive signals, make decisions, execute actions, and learn from the results over time.

That's exactly what Hellyeah was designed to do.

Unlike general-purpose AI frameworks that can be adapted for marketing, or traditional marketing platforms that have recently added AI features, Hellyeah was built from the ground up around the agentic marketing model. Instead of asking marketers to create workflows, monitor dashboards, and manually launch campaigns, it provides an autonomous execution layer that regularly operates across the entire growth stack.

Hellyeah's four core components work together through a shared data layer. Each one specializes in a different part of the marketing lifecycle while constantly exchanging data with the others, allowing the entire system to become more effective over time.

AIMA: The Autonomous Paid Acquisition Agent

Managing paid acquisition has traditionally required constant manual work. Marketing teams monitor campaign performance, adjust bids, pause underperforming ads, redistribute budgets, rotate creatives, and watch audience performance every day.

AIMA automates that operational layer.

Instead of waiting for someone to log into Google Ads or Meta Ads every morning, AIMA monitors campaign performance in real time. As conversion signals change, it reallocates budgets toward higher-performing audiences, adjusts bidding strategies, rotates creatives before fatigue reduces performance, and keeps campaigns aligned with the growth objectives defined by the team.

Marketing teams still define the overall acquisition strategy and business goals. AIMA handles the continuous optimization required to achieve them.

Mutation: The Behavioral Response Agent

Modern customer journeys rarely follow a predictable path. Some users become highly engaged within hours, while others quietly lose interest long before traditional lifecycle campaigns detect a problem.

Mutation is built for exactly those moments.

It monitors behavioral signals across both the product and marketing stack, looking for meaningful changes instead of waiting for scheduled workflows. A sudden drop in product usage, a customer reaching an activation milestone, repeated visits to a pricing page, or behavior that indicates purchase intent can all trigger immediate responses.

Instead of sending the same generic sequence to every user after seven days, Mutation responds to what each individual customer is doing right now. It can launch personalized re-engagement campaigns, activate lifecycle messaging, trigger product guidance, or initiate custom workflows the moment a meaningful behavioral signal appears.

Deja Vu: The Continuous Experimentation Agent

Most marketing teams run experiments in cycles.

Someone creates a hypothesis, launches an A/B test, waits for statistical significance, analyzes the results, publishes the winner, and eventually starts another experiment weeks later.

Deja Vu turns experimentation into a permanent system instead of a recurring project.

It runs multivariate experiments across landing pages, onboarding experiences, email campaigns, ad creatives, pricing pages, and other customer touchpoints. Instead of waiting for marketers to manually launch the next test, it reallocates traffic toward higher-performing variations automatically while continuing to search for better combinations.

Because every experiment feeds into the next one, optimization compounds over time instead of restarting with each testing cycle.

Marketing teams no longer spend most of their time managing experiments.

They spend their time deciding what business questions are worth exploring while Deja Vu keeps improving execution underneath them.

Forge: The Custom Agentic Workflow Builder

No two companies operate exactly the same way.

Every company has unique sales motions, content strategies, approval processes, outbound sequences, SEO workflows, influencer programs, and internal operations that can't be solved by generic automation templates.

Forge exists to build those custom systems.

Instead of forcing teams into predefined workflows, Forge creates AI agents tailored to each organization's growth strategy. Companies can build autonomous workflows for content production, GEO and SEO operations, outbound prospecting, influencer outreach, user-generated content pipelines, partner activation, lead routing, and countless other marketing processes.

Once deployed, these workflows continue running autonomously while adapting to new signals from the rest of the Hellyeah ecosystem.

As the company's marketing strategy evolves, Forge evolves alongside it.

The Compound Growth Loop

The biggest advantage of Hellyeah isn't any individual product.

It's the fact that all four systems operate from the same shared data layer.

Signals collected by Mutation influence the decisions made by AIMA. Insights discovered through Deja Vu improve campaign optimization across paid acquisition. Forge has access to the same behavioral intelligence that powers the rest of the ecosystem, allowing custom workflows to react using identical real-time context.

Instead of relying on disconnected integrations, every component shares the same behavioral and performance data. Insights generated by one agent immediately become available to the others, allowing every future decision to benefit from previous outcomes.

This creates a compounding feedback loop:

Better signals produce better decisions.
Better decisions produce stronger execution.
Better execution generates richer learning.
Better learning produces even better signals.

Best For

Hellyeah is best suited for growth-stage and enterprise SaaS companies, B2B businesses, e-commerce brands, mobile apps, fintech, gaming, and education companies that want to build an autonomous growth operation instead of managing an ever-growing collection of disconnected marketing tools.

Pricing

Enterprise

Limitation

Hellyeah isn't a plug-and-play product that delivers value within a few hours. Like any serious agentic system, it requires clean event instrumentation, well-defined growth objectives, and proper onboarding before its autonomous workflows can operate at full capacity. Teams that invest in that foundation benefit from compounding improvements over time, but organizations expecting instant results without preparation will likely be disappointed.

Agentic Marketing vs. Traditional Marketing Automation: Real-World Examples

The easiest way to understand agentic marketing is to compare how everyday marketing work changes once AI agents become responsible for execution.

Marketing Task	Traditional Marketing Automation	Agentic Marketing (Hellyeah)
Paid campaign optimization	Marketing managers manually review campaign performance, adjust bids, redistribute budgets, and rotate creatives every few days.	AIMA regularly monitors campaign performance, reallocates budgets, adjusts bids, and rotates creatives in real time without waiting for human intervention.
User re-engagement	Lifecycle emails are scheduled based on predefined rules, such as sending an email seven days after inactivity.	Mutation detects declining engagement as soon as behavioral signals change and launches personalized re-engagement campaigns tailored to each user's activity.
Landing page optimization	Teams plan A/B tests manually, wait for statistical significance, and publish winners before starting another experiment.	Deja Vu constantly runs multivariate experiments, reallocates traffic toward better-performing variants, and keeps optimizing without restarting the testing cycle.
Content and SEO execution	Content calendars are planned quarterly, briefs are created manually, and publishing follows fixed schedules.	Forge can build autonomous content pipelines that identify opportunities, generate briefs, coordinate production, and adapt publishing priorities as search behavior changes.
Outbound prospecting	SDRs manually research prospects, write personalized emails, and manage follow-up sequences themselves.	Forge builds agentic outbound workflows that research accounts, personalize outreach, coordinate follow-ups, and synchronize customer data automatically.

These examples illustrate the core difference between automation and agency.

Automation executes instructions.

Agentic marketing determines what the next instruction should be based on the latest information available.

What Agentic Marketing Is Not

As more companies begin using the term agentic marketing, it's easy to confuse it with existing marketing technologies. Understanding what it isn't is just as important as understanding what it is.

It isn't marketing automation with a new name.

Traditional marketing automation executes workflows that humans configure in advance. If a user performs a specific action, the system follows the rule that someone previously created.

Agentic marketing works differently. AI agents continuously evaluate the current situation, choose the most appropriate action based on context, execute it, and learn from the outcome. The difference isn't better automation; it's autonomous decision-making.

It isn't a replacement for marketing strategy.

AI agents can execute campaigns, optimize budgets, personalize customer journeys, and run experiments, but they don't define your company's positioning, messaging, product vision, or long-term business goals.

Marketers don't disappear; their role evolves. Instead of spending hours adjusting campaigns or maintaining workflows, teams focus on strategy, creativity, brand positioning, and growth priorities while AI handles operational execution.

It isn't plug-and-play software.

Building an autonomous marketing operation requires preparation.

Purpose-built platforms like Hellyeah need clean event instrumentation, connected marketing systems, clear business objectives, and an onboarding phase before autonomous agents can make high-quality decisions.

Companies willing to invest in that foundation benefit from a system that keeps improving over time. Those expecting instant results without proper setup will almost certainly be disappointed.

Frequently Asked Questions

What is agentic marketing?

→ Agentic marketing (also called autonomous marketing automation) is an AI-driven operating model where intelligent agents monitor signals, make decisions, execute marketing actions, and learn from the results. Unlike traditional automation, it adapts constantly based on real-time context instead of following fixed workflows.

How is agentic marketing different from marketing automation?

→ Marketing automation follows rules that humans create in advance, such as "if X happens, do Y." Agentic marketing goes further by evaluating current conditions, deciding the best action, executing it autonomously, and improving future decisions through continuous learning.

What are some real-world examples of agentic marketing?

→ Examples include AI agents that optimize paid campaigns in real time, detect customer churn signals and launch personalized re-engagement campaigns, constantly improve landing pages through experimentation, and automate outbound or SEO workflows without manual intervention.

Which platform is best for agentic marketing?

→ The right platform depends on how mature your marketing operation is. If you're building a fully agentic marketing system instead of adding AI to existing workflows, Hellyeah is purpose-built around that operating model. Its AIMA, Mutation, Deja Vu, and Forge components cover signal detection, decision-making, execution, and continuous learning within a unified system.

Final Thoughts

Agentic marketing represents one of the biggest architectural shifts marketing has seen since the rise of marketing automation.

Instead of managing dozens of disconnected tools and manually coordinating campaigns, organizations are beginning to operate unified AI systems that perceive signals, make decisions, execute actions, and continuously learn from every customer interaction.

Hellyeah embodies that shift. Instead of adding AI features to existing marketing software, it was purpose-built around the four layers that define an agentic marketing system: real-time signal detection through Mutation, autonomous paid acquisition with AIMA, continuous experimentation through Deja Vu, and custom growth workflows powered by Forge.

That doesn't eliminate the need for marketers.

It frees them to focus on what humans do best: strategy, creativity, positioning, and building products people genuinely want, while autonomous agents handle the operational complexity of modern growth.

Thanks for reading! 🙏🏻 Please follow Hadil Ben Abdallah & Hellyeah for more 🧡

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Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

BrowserAct vs Agent Browser: A Hands-On Stealth Execution Comparison

Hadil Ben Abdallah — Thu, 16 Jul 2026 07:45:58 +0000

Cloudflare and SannySoft benchmark results

Hadil Ben Abdallah

Jul 13

BrowserAct vs Agent Browser: A Hands-On Stealth Execution Comparison

#ai #agents #webdev #automation

10 min read

AI Customer Success Tools: 7 Platforms That Reduce SaaS Churn and Drive Expansion Revenue in 2026

Hadil Ben Abdallah — Tue, 14 Jul 2026 09:10:36 +0000

Companies with a net revenue retention (NRR) rate above 120% grow three times faster than those below 100%, according to KeyBanc Capital Markets’ SaaS Survey, making customer success one of the highest-leverage growth functions in modern SaaS.

Most SaaS teams still treat customer success as a reactive function: monitor accounts, review health scores, schedule check-ins, and respond when something goes wrong. But the companies scaling efficiently in 2026 are moving toward AI-powered customer success tools that detect behavioral signals, identify expansion opportunities, and trigger the right action before a human review is needed.

This guide compares the 7 best AI customer success platforms (also called CS automation platforms) for SaaS teams that want to improve Net Revenue Retention (NRR), reduce SaaS churn, and create more predictable expansion revenue.

Retention vs. Expansion: Why Customer Success Needs to Own Both

Customer success in 2026 is not only about preventing churn. The highest-performing teams manage two connected outcomes: protecting existing revenue and creating expansion revenue from customers who are already receiving value.

The metric that captures both is Net Revenue Retention (NRR).

NRR measures how much revenue remains from an existing customer base after accounting for expansion, churn, and contraction. A SaaS company with an NRR above 100% can grow even without acquiring new customers because existing accounts are generating additional revenue over time.

The formula is:

NRR = (Starting MRR + Expansion MRR - Churn MRR - Contraction MRR) / Starting MRR × 100

Traditional customer success workflows usually focus on the negative side of the equation: finding unhappy customers before they leave. AI customer success tools expand that view by identifying both risk signals and growth signals.

A declining login frequency, reduced feature usage, or increased support volume may indicate churn risk. But reaching a usage limit, adding teammates, or repeatedly engaging with advanced features may indicate an expansion opportunity.

The timing matters.

The worst moment to introduce an upgrade conversation is during renewal, when customers are already evaluating whether they should continue. The strongest expansion moments happen when users demonstrate value, hitting a feature limit, inviting more teammates, or adopting a workflow that naturally requires a higher plan.

AI-powered CS platforms help identify those moments automatically and connect them to the right intervention.

The Customer Health Score Framework: What AI Customer Success Tools Monitor

A strong customer health score is not just a dashboard metric. It is a combination of behavioral signals that shows whether an account is moving toward retention, expansion, or risk.

The best CS teams combine product usage data, customer feedback, support interactions, and revenue signals to create a complete picture of account health.

Health Signal	Data Source	Weight in Health Score	CS Action When Score Drops
Product engagement depth	Product analytics tools like Mixpanel and Amplitude	High (25–30%)	Trigger feature adoption guidance or targeted CSM outreach
Login frequency and session length	Product event stream	High (20–25%)	Launch re-engagement workflow or flag account risk
Support ticket volume and sentiment	Support platforms like Intercom	Medium (15–20%)	Escalate support issues and prioritize outreach
NPS / CSAT score	Customer feedback surveys	Medium (15%)	Contact detractors quickly and identify promoters
Seat utilization	CRM + product data	High (20–25%)	Detect contraction risk or expansion opportunities
Renewal proximity	CRM and billing data	Situational	Start renewal workflows and executive engagement
Expansion signals	Product events, feature usage, limits reached	Situational	Trigger expansion messaging at peak intent

The important difference between traditional CS reporting and AI-driven customer success is response speed.

A weekly health score review might show that an account has become unhealthy. A real-time behavioral system can detect multiple declining signals while they are happening and route the right action immediately.

AI Tools for SaaS Customer Success (2026 Comparison)

Tool	Category	Best For	Pricing Tier
Gainsight	Enterprise CS platform + health scoring + renewal management	Large SaaS companies with complex customer success operations	Enterprise
Hellyeah	Real-time post-activation behavioral tracking + expansion automation	SaaS teams wanting at-risk detection and expansion nudges to run autonomously	Enterprise
ChurnZero	Customer success + health scoring + expansion playbooks	Mid-market SaaS teams managing structured account portfolios	Paid / Enterprise
Totango	Modular CS platform + customer journey automation	Teams wanting flexible CS workflows without heavy implementation	Paid / Enterprise
Planhat	CS operations + revenue management	CS and RevOps teams aligning customer activity with revenue outcomes	Paid / Enterprise
Vitally	B2B SaaS CS platform + health scoring	Mid-market SaaS teams wanting faster deployment and usability	Paid
Intercom	Conversational CS + AI-assisted expansion messaging	SaaS teams using chat-led support and low-touch customer engagement	Paid (Free limited)

These customer success tools help SaaS teams move beyond reactive account management by combining behavioral signals, health scores, and AI-driven workflows.

Gainsight — Enterprise Customer Success Platform for Complex SaaS Operations

Gainsight is designed for SaaS companies where customer success has become a large operational function with dedicated teams, complex account structures, and multiple renewal workflows.

The platform acts as a central system of record by combining product usage data, CRM information, support interactions, and customer feedback into customer health scores. This gives CS leaders visibility across thousands of accounts and helps teams prioritize where human attention is required.

Its strength is operational depth. Large organizations can build renewal playbooks, QBR processes, escalation workflows, and executive engagement motions that standardize customer success across regions and teams.

Gainsight also includes AI capabilities through its Horizon AI layer, helping teams identify risks, recommend next actions, and automate certain customer success activities.

However, the complexity that makes Gainsight powerful also makes implementation demanding. Teams need dedicated CS operations resources to configure workflows, maintain integrations, and ensure adoption across customer-facing teams.

Best for: Enterprise SaaS companies with large CS organizations, complex renewal cycles, and multi-product account structures.

Limitation: Implementation requires significant time, operational resources, and investment. Smaller SaaS teams may not have enough complexity to justify the deployment effort.

Hellyeah — AI-Native Customer Success Automation for Retention and Expansion

Hellyeah AI is an AI-native growth engine that connects post-activation behavioral signals directly to autonomous retention and expansion actions.

Most CS platforms are designed around the workflow:

Collect data → calculate health score → notify the team → manually decide the next step

Hellyeah changes that loop into:

Detect signal → act immediately → learn from results → improve continuously

The core of this approach is Hellyeah’s Mutation layer, which monitors post-activation customer behavior and identifies changes that indicate either risk or expansion opportunity.

For example, if an account’s usage drops across multiple dimensions, fewer logins, lower feature adoption, and reduced team activity, Mutation can flag the account before a CSM notices it during a weekly review.

But the same mechanism works in the opposite direction.

When a customer reaches a feature limit, adds new teammates, or shows repeated usage of advanced functionality, Mutation can identify the expansion signal and trigger the right next step: an in-app upgrade prompt, personalized message, or CSM notification.

The difference is timing.

An expansion conversation sent during renewal is often too late because the customer has already formed an opinion about the product’s value. A message triggered when users actively experience value appears at the moment intent is highest.

Hellyeah’s other layers extend this beyond detection.

Mutation handles real-time behavioral detection and response.
Deja Vu continuously experiments with expansion and retention interventions. Instead of manually testing one upsell message every few months, Deja Vu evaluates which message, timing, and segment combination performs best and reallocates toward stronger variations.
Forge enables custom AI agentic workflows around unique CS operations, including health score calculations, escalation routing, QBR preparation, and account-specific processes.
AIMA extends the lifecycle beyond the product by enabling targeted campaigns for accounts that need additional reinforcement across channels.

Together, these components create a customer success operation that compounds over time. Fewer at-risk accounts, more expansion opportunities, and less manual analysis for customer success teams.

The result is not replacing CSMs. It is making every CSM interaction higher leverage by ensuring teams spend time on the accounts where human judgment matters most.

Best for: SaaS companies that want post-activation health monitoring, churn prevention, and expansion automation to run continuously without relying on manual account reviews.

Limitation: Hellyeah requires clean product event instrumentation and reliable customer data connections before it can deliver full value. Teams without a strong event taxonomy or structured CRM data will need to improve their data foundation first.

ChurnZero — Customer Success Platform for Mid-Market SaaS Teams

ChurnZero focuses on helping mid-market SaaS companies manage customer relationships through health scoring, automated playbooks, and account-level visibility.

The platform combines product usage, CRM data, and customer interactions to identify accounts that require attention. CS teams can create automated workflows for onboarding, adoption milestones, renewal preparation, and expansion opportunities.

Where ChurnZero performs well is structured customer success operations. Teams with dedicated CSMs can use it to manage portfolios, monitor account health, and create repeatable processes instead of relying on spreadsheets and manual tracking.

Its automation capabilities are particularly useful for companies managing hundreds of customer accounts where personalized attention is difficult to maintain manually.

However, ChurnZero is built around a CSM-led customer success model. Companies that rely primarily on product-led growth and self-service expansion may not benefit from all of its capabilities.

Best for: Mid-market SaaS companies with customer success teams managing structured account portfolios.

Limitation: Less effective for PLG companies without dedicated CSM workflows because its strongest features depend on human-led customer success motions.

Totango — Modular Customer Success Platform for Flexible CS Operations

Totango is designed for SaaS teams that need a customer success platform without adopting the complexity of a fully enterprise-focused system. Its modular approach allows teams to build customer journeys around specific lifecycle stages such as onboarding, adoption, renewal, and expansion.

The platform uses configurable SuccessBLOCs, which are pre-built frameworks for common customer success workflows. Teams can activate the modules they need, define health metrics, create playbooks, and automate customer interactions without rebuilding their entire CS operation from scratch.

This flexibility makes Totango attractive for growing SaaS companies that have moved beyond spreadsheets but are not ready for the operational overhead of large enterprise CS platforms.

Its customer journey capabilities are especially useful for teams managing different customer segments with different success criteria. A small business customer and an enterprise account can follow completely different engagement paths while still being managed from the same platform.

However, flexibility also creates a tradeoff. Teams often need to invest time defining their own processes, metrics, and workflows before they can extract maximum value.

Best for: SaaS companies that want a configurable customer success platform with modular workflows and faster adoption than traditional enterprise solutions.

Limitation: Large enterprises with highly complex account structures may eventually need deeper customization and broader integrations than Totango provides.

Planhat — Customer Success Operations Platform for Revenue Alignment

Planhat focuses on connecting customer success activities with measurable revenue outcomes. Instead of treating CS as a support function, it gives teams visibility into metrics that directly impact growth, including retention, expansion revenue, contraction, and customer health.

The platform combines customer data from CRM systems, product analytics, and billing platforms into customizable dashboards. This allows CS and RevOps teams to work from the same data foundation when forecasting renewals or identifying expansion opportunities.

One of Planhat’s strongest advantages is flexibility. Teams can customize workspaces, dashboards, and workflows around their specific operating model instead of adapting everything to a rigid structure.

For SaaS companies where customer success owns expansion revenue, this alignment is valuable because it creates clearer accountability between customer outcomes and revenue performance.

The tradeoff is that flexibility requires operational maturity. Teams without clear processes may spend significant time designing their own workflows instead of immediately benefiting from predefined best practices.

Best for: B2B SaaS companies where customer success and revenue operations need a shared system for retention and expansion planning.

Limitation: A smaller ecosystem of native integrations compared with larger enterprise platforms can require additional API work for complex data environments.

Vitally — Fast-to-Deploy Customer Success Platform for B2B SaaS Teams

Vitally is built for SaaS teams that need structured customer success operations without the long implementation cycles often associated with enterprise platforms.

It provides customer health scoring, account management workflows, task automation, and playbook functionality through a user experience designed around daily CSM workflows.

Its main advantage is speed. Teams can connect common SaaS data sources, configure customer health models, and start managing accounts without months of operational setup.

Vitally is particularly popular among B2B SaaS companies that have reached the stage where customer relationships require more structure but still want a platform that feels lightweight and easy for customer-facing teams to adopt.

The platform also supports automated workflows that help CSMs manage onboarding, renewal preparation, and customer engagement activities more consistently.

However, its simplicity comes with limitations. Companies with thousands of accounts, multiple product lines, and highly complex enterprise renewal processes may eventually need a more comprehensive enterprise CS system.

Best for: Growing B2B SaaS companies that need a modern customer success platform with faster deployment and strong usability.

Limitation: Less suitable for large enterprises requiring highly complex account hierarchies, advanced governance, and extensive renewal operations.

Intercom — Conversational Customer Success and AI-Assisted Engagement

Intercom approaches customer success from the conversation layer. Instead of acting primarily as a customer health database, it focuses on helping SaaS teams communicate with users through AI-powered support, messaging, and in-product interactions.

Its AI agent, Fin, helps resolve customer questions automatically, reducing support friction that can contribute to churn. Product tours and targeted messages also allow teams to guide users toward important features and adoption milestones.

For product-led SaaS companies, this conversational approach can be powerful because many customer interactions happen directly inside the product rather than through scheduled CSM calls.

Intercom can also support expansion conversations by identifying opportunities for targeted messaging based on user behavior and engagement patterns.

However, it is not a dedicated customer success operating system. Teams looking for portfolio-level health scoring, renewal forecasting, and expansion pipeline management will typically need additional CS infrastructure.

Best for: SaaS companies with chat-driven customer engagement models and teams that want AI-assisted support and in-product communication.

Limitation: It lacks deep customer success management capabilities such as account health scoring, renewal management, and revenue forecasting.

The NRR Optimization Playbook: What High-Performing CS Teams Do Differently

The best customer success teams in 2026 do not measure success only by how many accounts they save. They build systems that protect existing revenue while continuously creating expansion opportunities.

Net Revenue Retention (NRR) has become the central metric because it measures the complete customer lifecycle: what revenue stays, what revenue expands, and what revenue disappears.

A SaaS company with NRR above 100% can grow its revenue base even without acquiring new customers because existing customers are increasing their spending over time.

A modern CS operation should focus on five principles:

1. Define NRR as the customer success north star

Churn rate only tells you what was lost. NRR shows the complete picture by combining retention and expansion.

Customer success teams that optimize only for churn reduction often miss opportunities to grow existing accounts. Expansion revenue from additional seats, upgraded plans, and new product adoption should be treated as a core CS responsibility.

2. Build health scores that predict expansion, not only risk

Most health scores answer one question:

“Which customers might leave?”

Advanced CS teams ask a second question:

“Which customers are ready to grow?”

Signals such as increased feature usage, new team members, and approaching usage limits can indicate expansion opportunities before a customer explicitly asks for an upgrade.

3. Automate the long tail and focus humans where they matter

CS teams cannot manually review every account every day.

AI customer success tools can monitor behavioral signals, update health scores, and trigger workflows automatically. This allows CSMs to spend more time on strategic conversations with high-value accounts.

The goal is not replacing customer success managers. It is increasing the leverage of every interaction.

4. Continuously test expansion messaging

The best expansion strategy today may not be the best strategy six months from now.

Customer behavior changes, markets shift, and different segments respond differently. AI experimentation layers like Hellyeah's Deja Vu help teams continuously test which message, timing, and channel creates the strongest expansion response.

5. Connect CS operations with revenue teams

Expansion revenue should not exist as an informal opportunity hidden inside customer conversations.

High-performing SaaS companies connect customer success data with revenue operations so expansion opportunities become visible pipeline instead of unexpected wins.

Frequently Asked Questions

What is the best AI tool for SaaS customer success in 2026?

→ The best AI customer success tool depends on company size, customer model, and CS maturity. Enterprise teams often use Gainsight, while mid-market SaaS companies may prefer ChurnZero or Vitally.
For autonomous post-activation monitoring and expansion optimization, Hellyeah AI combines behavioral detection, workflow automation, and experimentation through Mutation and Deja Vu.

What is Net Revenue Retention (NRR) and why does it matter for SaaS companies?

→ Net Revenue Retention (NRR) measures how much revenue a SaaS company keeps and expands from existing customers over time.
It includes expansion revenue, upgrades, churn, and contraction, making it a stronger growth metric than churn rate alone. An NRR above 100% means the existing customer base is growing without new acquisition.

What is the difference between customer retention and customer success?

→ Customer retention focuses on preventing churn by identifying risks and keeping existing customers active.
Customer success takes a broader approach by improving adoption, helping customers achieve value, and creating expansion opportunities.
Retention prevents loss, while customer success drives long-term growth and revenue expansion.

How do AI tools improve customer success team efficiency?

→ AI customer success tools automate manual account reviews by continuously analyzing product usage, support activity, and CRM data.
They detect behavioral signals earlier and help teams prioritize the right actions.
Tools like Hellyeah's Mutation enable real-time responses, while Deja Vu improves engagement through continuous experimentation.

Final Thought

Customer success is no longer about creating more dashboards and hoping teams discover problems faster.

The highest-performing SaaS companies build systems that detect behavioral changes automatically, identify expansion opportunities at the right moment, and route every signal to the right action.

The future of customer success is not more manual account reviews. It is intelligent infrastructure that helps every CSM focus on the conversations where human judgment creates the most value.

Thanks for reading! 🙏🏻 Please follow Hadil Ben Abdallah & Hellyeah for more 🧡

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Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

BrowserAct vs Agent Browser: A Hands-On Stealth Execution Comparison

Hadil Ben Abdallah — Mon, 13 Jul 2026 09:11:03 +0000

A hands-on comparison where I tested BrowserAct and Agent Browser using the SannySoft browser fingerprint test and the Cloudflare Challenge benchmark to evaluate their stealth execution and anti-bot capabilities under identical conditions.

You switched to a browser automation tool built specifically for AI agents.

That should have solved the hardest part... right?

After all, these tools are designed for production workflows, autonomous agents, and modern browser automation. So you launch your agent against a website protected by Cloudflare, expecting everything to work.

Instead, you find yourself in front of another verification page. Or the browser keeps asking you to verify you're human. Or the automation simply never reaches the content you need.

So I was asking myself:

If both BrowserAct and Agent Browser are built for AI agents, how different are they when it comes to stealth execution and anti-detection?

Instead of comparing documentation or feature lists, I decided to run both tools through the same hands-on tests under the same conditions.

For this comparison, I focused on one capability: stealth execution.

I tested both tools against the two most widely used benchmarks in the automation community.

SannySoft for browser fingerprint detection
Cloudflare Challenge for real-world anti-bot protection

These two tests measure different aspects of browser automation, and together they provide a clearer overview than a simple feature comparison.

I'll walk through exactly what I observed during both tests, where each tool performed well, where I noticed differences, and which one I would choose depending on the type of automation I'm building.

Before getting into the results, it's important to understand why stealth execution has become one of the biggest challenges in modern browser automation.

Why Stealth Execution Matters in Modern Browser Automation

A few years ago, getting browser automation working mostly meant writing reliable scripts.

If your selectors were correct and your timing was good, there was a good chance your automation would work consistently.

Today, that's only half of the challenge.

Modern websites don't just respond to browser actions. They also evaluate the browser itself almost immediately after the page starts loading.

That creates two different layers of anti-bot protection.

The first layer is browser fingerprint detection.

This focuses on identifying whether the browser behaves like a normal user or an automated environment. Websites inspect signals such as:

navigator.webdriver
browser plugins
user agent
WebGL renderer
Chrome object
other browser fingerprint characteristics

Many of these checks happen before your automation even clicks its first button.

The second layer is real-world anti-bot protection.

Services such as Cloudflare combine browser fingerprinting with additional signals like browser behavior, challenge-response verification, network reputation, and other detection techniques before deciding to trust a session.

That's an important distinction because passing a fingerprint test doesn't automatically mean a browser will get through Cloudflare or similar protection systems.

This is exactly why I used two different benchmarks for this article.

On paper, both BrowserAct and Agent Browser offer stealth capabilities.

The important thing is how they approach it. And that difference isn't obvious from their documentation.

It becomes easier to understand the difference when you run the same tests against both tools.

BrowserAct vs Agent Browser: Stealth Approach Before Testing

Before running the benchmarks, I wanted to understand how each tool approaches browser execution in environments that actively inspect automation.

Although both tools target AI agent workflows, they expose their browser environments differently.

BrowserAct

BrowserAct provides a dedicated stealth browser that is ready to use once you've created a stealth browser profile.

According to BrowserAct's documentation, its stealth browser is designed to reduce common automation signals by providing characteristics such as:

WebDriver not exposed
Realistic plugin surface
Chrome user agent instead of HeadlessChrome
Hardware-like WebGL rendering
Reduced Chrome DevTools Protocol (CDP) detection signals
A browser fingerprint designed to resemble a regular browsing session

To launch the browser, I simply opened my existing stealth browser profile:

browser-act --session stealth-test browser open <browser-id> https://example.com --headed

No additional stealth configuration was required before running my tests.

Agent Browser

For Agent Browser, I used the standard CLI installation and launched a browser session directly without adding any extra plugins or manually modifying the browser fingerprint.

The browser was started with:

agent-browser --session stealth-test open https://example.com --headed

Since my goal was to compare the tools using their default workflows after installation, I intentionally avoided adding third-party extensions or making manual browser modifications.

Hands-On Testing Methodology

To keep the comparison as fair as possible, I tested both tools under the same environment using fresh test runs for this article.

Test Environment

Windows
BrowserAct CLI (using a built-in stealth browser profile)
Agent Browser CLI (default installation)
Chromium-based browsers

Why SannySoft and the Cloudflare Challenge were selected

I selected two benchmarks because they evaluate different aspects of browser automation.

bot.sannysoft.com checks browser fingerprint characteristics, including WebDriver detection, browser plugins, user agent characteristics, Chrome object availability, WebGL information, and several other browser signals.
scrapingcourse.com/cloudflare-challenge simulates a real Cloudflare-protected page and shows whether the browser can successfully reach the protected content.

Together, these tests provide a practical view of both browser detectability and real-world behavior.

With both environments ready, I moved on to the fingerprint and Cloudflare challenge tests.

Test 1: Browser Fingerprint Detection (SannySoft)

The first benchmark I ran was SannySoft.

Instead of checking whether a browser can bypass a specific anti-bot provider, SannySoft focuses on the browser fingerprint itself.

I ran the test separately with BrowserAct and Agent Browser using fresh browser sessions.

BrowserAct Test

For BrowserAct, I opened a new stealth browser session and navigated directly to SannySoft.

browser-act --session fingerprint browser open <browser-id> https://bot.sannysoft.com --headed

BrowserAct running the SannySoft browser fingerprint test on the terminal

BrowserAct on SannySoft showing the browser fingerprint results

BrowserAct also provides a useful CLI command (state) that lets you inspect the current page directly from the terminal.

browser-act --session fingerprint state

Inspect SannySoft test result directly from the terminal

The report looked very clean.

The key observations I noted were:

WebDriver: Passed
Chrome object: Present
Plugin detection: Passed
Browser fingerprint appeared consistent with a normal browser session

BrowserAct passed all of the major fingerprint checks reported by SannySoft during my testing.

The browser exposed very few characteristics that would immediately identify it as automation.

Agent Browser Test

For Agent Browser, I also launched a browser session directly.

agent-browser --session fingerprint open https://bot.sannysoft.com --headed

Agent Browser running the SannySoft browser fingerprint test on the terminal

Agent Browser on SannySoft showing the browser fingerprint results

The overall report was fairly strong, but one result immediately stood out.

The key observations were:

WebDriver: Failed
Chrome object: Present
Plugin detection: Passed
Most browser fingerprint checks passed successfully

From my test, WebDriver was the only major fingerprint check that failed. The remaining fingerprint characteristics appeared much closer to a regular browser session.

Side-by-Side Results

Fingerprint Check	BrowserAct	Agent Browser
WebDriver	✅ Passed	❌ Failed
Chrome Object	✅ Present	✅ Present
Plugin Detection	✅ Passed	✅ Passed
Overall Fingerprint	Passed all major checks	Failed WebDriver

What I Observed

Before running the test, I expected both tools to perform similarly because both advertise stealth capabilities.

BrowserAct passed all of the major checks reported by SannySoft during my test, while Agent Browser exposed one remaining automation indicator through the WebDriver check.

Of course, browser fingerprinting is only one layer of modern anti-bot detection, and websites evaluate many more signals before deciding whether to trust a browser session.

But, still, this benchmark gave me a good first impression. BrowserAct presented a browser fingerprint that looked slightly closer to a regular user session based on the checks reported by SannySoft.

Now, let's see how both tools would behave when facing an actual anti-bot challenge instead of a diagnostic website.

Test 2: Cloudflare Challenge

Fingerprint tests are useful because they reveal how detectable a browser is.

That's why I also tested both tools against the Cloudflare Challenge page provided by ScrapingCourse.

Unlike SannySoft, this isn't a diagnostic report. The browser either reaches the protected page or it doesn't.

BrowserAct Test

I opened a fresh BrowserAct stealth browser session and navigated directly to the Cloudflare challenge page.

browser-act --session cloudflare browser open <browser-id> https://www.scrapingcourse.com/cloudflare-challenge --headed

BrowserAct on Cloudflare showing the browser anti-bot result on terminal

Within a few moments, the verification completed successfully.

The page displayed:

BrowserAct successfully reaching the protected page

"You bypassed the Cloudflare challenge! :D"

I inspected the current page again directly from the terminal using:

browser-act --session cloudflare state

Inspect Cloudflare test result directly from the terminal

Unlike several Cloudflare tests I've previously run with other browser automation tools, this session didn't get stuck repeatedly asking for additional verification.

It simply completed the challenge and proceeded to the protected content.

That was the outcome I was hoping to evaluate with this benchmark.

Next, I repeated the same test using Agent Browser under the same conditions.

Agent Browser Test

For Agent Browser, I followed the same process and opened the Cloudflare challenge page using a fresh browser session.

agent-browser --session cloudflare open https://www.scrapingcourse.com/cloudflare-challenge --headed

Agent Browser on Cloudflare showing "Just a moment" on terminal

The result was noticeably different from BrowserAct.

Instead of reaching the protected content, the browser remained on Cloudflare's verification screen.

On the terminal, it displayed:

"Just a moment"

And on the Cloudflare page, it repeatedly displayed:

"Verify you are human"

As shown in the GIF below, the challenge kept refreshing, and even after 4 attempts, it never progressed to the protected page.

Agent Browser keeps showing "Verify you are human"

Unlike BrowserAct, which completed the verification during my first attempt, Agent Browser remained stuck in the verification loop throughout the test.

I also kept the terminal running during the test to make sure nothing else was happening in the background.

Side-by-Side Results

Cloudflare Challenge	BrowserAct	Agent Browser
Challenge completed	✅ Yes	❌ No
Protected page reached	✅ Yes	❌ No
Verification loop	❌ No	✅ Yes
Result during my test	Passed on first attempt	Continued asking for verification

What I Observed

BrowserAct completed the verification and reached the protected page on my first attempt.

Agent Browser, on the other hand, never moved beyond Cloudflare's verification screen during my testing. The browser continued asking me to verify that I was human and never reached the protected content.

Of course, Cloudflare constantly updates its detection systems, so no single test guarantees that a browser will always succeed or always fail in every environment.

Still, these were the results I consistently observed while running both tools under the same conditions.

BrowserAct vs Agent Browser: Test Results and Comparison

These observations come directly from what I saw during the tests.

Capability	BrowserAct	Agent Browser
SannySoft fingerprint test	Passed all major checks	Failed the WebDriver check
Cloudflare Challenge	Reached the protected page	Remained on the verification screen
Setup experience	Built-in stealth browser	Default CLI browser session
Overall experience during testing	Worked smoothly in both benchmarks	Good fingerprint results, but struggled with Cloudflare

The important difference for me was what happened when the browser reached a protected website.

The fingerprint benchmark showed only one failed check for Agent Browser, which initially made me think the Cloudflare test might produce similar results.

Instead, the two tools behaved very differently.

Which Tool Should You Choose?

Both BrowserAct and Agent Browser are designed for AI-powered browser automation, but based on my testing, they currently perform differently when stealth execution is the primary concern.

BrowserAct is a better fit if you:

regularly work with websites protected by Cloudflare or similar anti-bot systems
need the strongest browser fingerprint possible from the initial setup
prioritize getting through anti-bot protection over browser customization

During my testing, BrowserAct passed every major SannySoft fingerprint check and successfully completed the Cloudflare challenge test on the first attempt. If those are the kinds of environments you work with every day, that was a meaningful advantage.

Agent Browser is worth considering if you:

are already building AI agent workflows around its ecosystem
don't primarily target heavily protected websites

In my tests, Agent Browser produced a good fingerprint overall, with the exception of the WebDriver check. The larger limitation appeared during the Cloudflare benchmark.

That doesn't necessarily mean it will struggle with every protected website, but it was the outcome I consistently observed.

So the right choice depends on the problems you're trying to solve.

Final Thoughts

Before starting this comparison, I expected the two tools to produce fairly similar results.

Since both are built for AI agents and both offer stealth capabilities, I assumed the differences would mostly come down to developer experience or workflow preferences.

After running the tests, I found different results.

The browser fingerprint benchmark already showed a small difference between the two tools, but the Cloudflare challenge test made the distinction more obvious.

So, it's easy to compare features on a website or read through documentation, but running the same benchmark under the same conditions often makes the differences much clearer.

Of course, anti-bot systems evolve constantly, and no browser automation tool will succeed against every protected website forever. Results can also vary depending on the target site, browser version, network environment, and future updates to both tools.

These findings simply reflect what I observed while testing BrowserAct and Agent Browser side by side.

If you'd like to reproduce the same tests I ran in this article, you can try BrowserAct yourself. BrowserAct is also running a promotion at the moment: if you star the GitHub repository, you'll receive 500 free credits to help you get started with your own experiments.

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

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Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

Best AI Tools for SaaS Customer Retention: How to Stop Churn Before It Starts (2026 Guide)

Hadil Ben Abdallah — Wed, 08 Jul 2026 09:35:26 +0000

According to the PLG AI SaaS Benchmarks 2026 report, SaaS companies lose an average of 5–7% of revenue every month to churn, a rate that quietly compounds into nearly half of annual revenue erosion if left unchecked.

Most teams don’t realize churn is already happening long before the cancellation click. It starts as subtle behavioral drift, lower engagement, feature abandonment, and delayed logins and only shows up in dashboards when it’s too late to act.

That’s where AI changes the equation. Instead of reacting to churn, modern SaaS teams now try to intercept it through real-time behavioral detection, automated interventions, and continuous experimentation inside the product.

Here are the best AI tools for SaaS customer retention (also called churn prevention tools) in 2026, compared by category, pricing, and key limitation.

Why Traditional Churn Prevention Fails

Most churn prevention strategies fail for three predictable reasons.

First, they rely on lagging indicators. By the time dashboards show declining engagement, the user has already mentally churned. The decision didn’t happen when they clicked cancel; it happened days or weeks earlier during silent disengagement.

Second, interventions are batch-based. Many lifecycle tools still operate on schedules like “send email after 7 days of inactivity.” But churn signals don’t wait for weekly jobs. The best intervention window is the moment behavior changes.

Third, messaging is too generic. A user abandoning reporting features needs a completely different response than one abandoning collaboration workflows. Yet most tools treat both cases the same.

The result is simple: teams react too late, too slowly, and too generically.

Churn Signal Framework (What Predicts Churn)

Churn doesn’t appear randomly; it follows patterns that can be detected in product data before cancellation ever happens.

Churn Signal	What It Looks Like	Intervention Window	Best Response
Login drop	Daily user becomes inactive within 7–14 days	1–7 days after drop	Contextual re-engagement tied to last-used feature
Feature abandonment	Core feature usage drops >50%	1–5 days	Targeted in-app guidance or outreach
Support spike	Multiple tickets in short period	Same day	Proactive support + escalation
Onboarding stall	No activation milestone after signup	7–14 days	Guided activation flow
Seat decline	Multi-user account loses active seats	1–10 days	Account-level alert + outreach

The key insight is timing. Most churn signals appear 2–6 weeks before cancellation, which creates a narrow but critical intervention window.

AI Tools for SaaS Customer Retention (2026 Comparison)

Tool	Category	Best For	Pricing Tier
ChurnZero	Customer success + churn prediction	Mid-market SaaS with dedicated CSM teams	Paid / Enterprise
Hellyeah	Real-time behavioral detection + autonomous retention response	SaaS teams wanting churn signals acted on instantly without manual workflows	Enterprise
Gainsight	Enterprise CS + health scoring	Large SaaS orgs with complex renewal processes	Enterprise
Intercom	Conversational retention + support automation	Reducing support-driven churn via AI chat + messaging	Paid (limited free tier)
Mixpanel	Behavioral analytics	Understanding churn patterns through product usage data	Free / Paid
Customer.io	Lifecycle messaging automation	Event-triggered retention campaigns across channels	Paid
Pendo	In-app guidance + adoption analytics	Improving onboarding and feature adoption	Paid / Enterprise
Amplitude	Product analytics + retention insights	Cohort analysis and retention modeling	Free / Paid

These customer retention tools represent the 2026 standard for reducing SaaS churn, improving net revenue retention (NRR), and identifying behavioral signals early enough to act before users disengage.

ChurnZero — Customer Success Platform for Account-Based Retention

ChurnZero is built for SaaS teams that manage retention at the account level rather than the individual user level. It aggregates product usage, CRM data, and support signals into structured health scores that help CSMs prioritize outreach.

Where it becomes valuable is in mid-market SaaS environments where customer success teams actively manage renewals. It gives visibility into which accounts are expanding, stagnating, or at risk and ties that directly to action playbooks.

However, its real strength depends on human execution. The platform surfaces insights and risk signals, but it assumes a team of CSMs will act on them. Without that layer, much of its intelligence remains underused.

Limitation: Less effective for product-led SaaS companies without a dedicated customer success motion.

Hellyeah — Real-Time Retention Execution Layer

Hellyeah AI is the only platform in this list designed to close the loop between churn detection and action in real time.

Most retention tools detect risk and notify humans. Hellyeah’s Mutation layer removes that delay entirely by reacting the moment behavioral drift appears.

When a user’s engagement drops, for example, from daily usage to near inactivity, Mutation doesn’t wait for a report. It immediately triggers a contextual intervention: an in-app message, lifecycle email, CSM alert, or upgrade prompt based on the user’s behavior history.

That difference matters because churn is not a sudden event. It is a gradual loss of intent that can be reversed only while the user is still in that decision window.

Beyond detection and response, Hellyeah operates as a compound system:

Mutation handles real-time behavioral detection and response
Deja Vu continuously tests which interventions work best per churn signal
Forge builds custom workflows like health scoring, escalation routing, and retention logic
AIMA can re-acquire churned users through targeted paid campaigns

Instead of static workflows, Hellyeah creates a closed-loop retention system: detect → act → learn → improve.

Limitation: Hellyeah depends heavily on proper event instrumentation. If your product data is incomplete or inconsistent, the system cannot reliably interpret user behavior. It is not a plug-and-play tool; it requires setup before it becomes fully effective.

Gainsight — Enterprise-Grade Customer Success System

Gainsight is designed for large-scale SaaS organizations where customer relationships span multiple products, stakeholders, and renewal cycles. It brings together product data, CRM signals, and support interactions into a unified health scoring system.

Its biggest advantage is operational depth. Enterprises can build structured renewal playbooks, QBR workflows, and escalation systems that scale across thousands of accounts.

But that depth comes with complexity. Implementation is heavy, and teams often require months before the system is fully operational. It is powerful, but not lightweight.

Limitation: High implementation cost and long setup cycles make it unsuitable for early-stage or lean PLG teams.

Intercom — Conversational Retention and Support Automation

Intercom focuses on reducing churn caused by support friction. Its AI agent, Fin, resolves user questions in real time, while messaging tools help re-engage users based on behavioral triggers.

This combination is particularly effective for SaaS products where confusion or lack of support is a major driver of churn. When users get stuck, Intercom reduces resolution time dramatically, preventing abandonment.

It also enables proactive messaging inside the product, allowing teams to reach users before frustration escalates into churn.

However, as usage grows, pricing can scale quickly depending on resolution volume and seat count, which impacts predictability for high-traffic products.

Limitation: Cost scales significantly with usage, making it less predictable at high volume.

Mixpanel — Behavioral Analytics for Churn Detection

Mixpanel is a core analytics layer in many retention stacks. It helps teams understand how users behave inside the product and which actions correlate with long-term retention.

Its strength lies in funnel analysis and cohort comparison. Teams can see exactly where users drop off and identify behavioral patterns that precede churn. This makes it essential for defining what “at-risk” actually looks like.

However, Mixpanel stops at insight. It does not trigger interventions or engage users directly, which means it must be paired with execution tools to close the loop.

Limitation: Analytics-only platform with no built-in activation or response capabilities.

Customer.io — Event-Based Retention Messaging

Customer.io is built for lifecycle messaging triggered by real-time product events. It allows teams to design automated retention flows across email, push, SMS, and in-app channels.

Its visual workflow builder makes it flexible for creating complex branching logic based on user behavior. This is especially useful for retention campaigns tied to specific engagement patterns or milestones.

The tradeoff is setup complexity. Every workflow must be designed manually, which requires planning and ongoing maintenance as product behavior evolves.

Limitation: Requires significant manual configuration to build and maintain effective workflows.

Pendo — In-App Adoption and Guidance Layer

Pendo helps improve retention by guiding users toward key features through in-app messaging, walkthroughs, and tooltips. It is especially effective during onboarding, where early feature discovery strongly influences retention outcomes.

It also connects product analytics with in-app experiences, allowing teams to identify friction points and address them directly inside the product interface.

However, it is less effective for real-time churn intervention. It works best in structured onboarding flows rather than reactive retention scenarios.

Limitation: Limited real-time churn response capability.

Amplitude — Retention Intelligence and Cohort Analysis

Amplitude helps teams understand retention at a deeper level by analyzing user cohorts and behavioral patterns over time. It highlights which actions correlate most strongly with long-term retention.

Its predictive insights allow teams to identify early activation milestones that correlate with success. This is particularly useful for product-led companies optimizing onboarding and engagement flows.

However, like other analytics tools, it does not execute interventions, meaning it must be paired with a response layer to act on its insights.

Limitation: Insight-only platform with no built-in execution layer.

How to Build a Modern Churn Prevention Stack

A strong retention system is built in layers, not tools.

Start by instrumenting product events so every meaningful user action is tracked consistently. Without this, no retention system can function properly.

Then use analytics platforms to identify churn signals, the behavioral patterns that reliably precede cancellation.

Next, introduce a real-time response layer that acts immediately when those signals appear, closing the gap between detection and intervention.

For teams with customer success operations, add account-level platforms that surface high-value risks for human follow-up.

Finally, continuously refine interventions using experimentation so retention strategies improve over time rather than stagnating.

Frequently Asked Questions

What is the best AI tool for SaaS customer retention?

→ The best tool depends on your company structure. Product-led teams benefit most from real-time systems like Hellyeah AI, while enterprise teams often rely on Gainsight or ChurnZero. The most effective setups combine analytics with real-time response layers.

What are early signs of SaaS churn?

→ Early churn signals include declining login frequency, reduced feature usage, support spikes, and failure to reach activation milestones. These patterns usually appear weeks before cancellation and can be intercepted with the right tooling.

Why do most retention strategies fail?

→ Most strategies fail because they act too late. They rely on batch processing and generic messaging instead of responding in real time to behavioral changes. By the time action is taken, the user has already disengaged.

Do analytics tools reduce churn by themselves?

→ No. Tools like Mixpanel and Amplitude help identify churn patterns, but they don’t take action. They must be paired with execution systems that can intervene based on the insights they surface.

Final Thought

Churn is not a sudden decision; it’s a slow behavioral exit that starts long before most teams notice it.

The companies that reduce churn most effectively are the ones that detect behavioral changes while users are still active, not after those changes appear in weekly reports. For example, a drop in login frequency or a 50% decline in core feature usage often appears days or weeks before cancellation, creating an opportunity to intervene before the customer decides to leave.

Modern SaaS retention is about detecting churn signals in real time, triggering personalized interventions immediately, and continuously improving those interventions as new behavioral data comes in.

Thanks for reading! 🙏🏻 Please follow Hadil Ben Abdallah & Hellyeah for more 🧡

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Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

AI Governance for Engineering Teams: Guardrails, Budgets, and Audit Logs That Actually Scale

Hadil Ben Abdallah — Tue, 07 Jul 2026 11:31:25 +0000

Most AI incidents don't happen because the model gave a bad answer. They happen because nobody was governing everything around the model.

Large language models are already finding their way into everyday engineering workflows. Developers use them in IDEs, support teams rely on them to answer customer questions, analysts connect them to internal databases, and AI agents now execute tasks that used to require direct human involvement.

Getting AI into production becomes easy. The harder problem is keeping it reliable, secure, and manageable after deployment.

The problem usually becomes obvious after deployment. The first few AI integrations feel manageable, but as more models, tools, MCP servers, and applications are introduced, questions that were simple become difficult to answer.

Which AI applications are allowed to access production systems?
Which MCP tools can each agent invoke?
Who approved those permissions?
How much is every team spending on AI?
Can you trace every model request back to a specific user?

Without clear answers, AI usage can expand faster than the processes needed to manage it.

This is the role AI governance plays.

Contrary to what many people assume, AI governance isn't about slowing developers down or surrounding AI with unnecessary bureaucracy. Good governance should feel almost invisible. It provides guardrails that let engineering teams move quickly while ensuring security, compliance, and cost remain under control.

This is exactly the problem Bifrost AI Gateway was designed to solve.

Instead of adding separate controls for every AI service, Bifrost provides a centralized control plane that manages every AI request before it reaches a model or an MCP server.

As organizations expand AI across teams, that single layer becomes important.

Why AI Governance Has Become Essential for Engineering Teams

Most organizations don't start with an AI governance strategy.

They start with one successful experiment.

Maybe someone connects Claude Code to a few internal tools. Another team builds an AI support assistant. Someone else integrates GPT into a Slack workflow. Before long, different departments are choosing different models, different providers, different prompts, and different MCP servers.

Eventually, the same operational problems start showing up:

AI spending becomes difficult to track across teams and providers.
Agents gain access to internal systems without clear approval processes.
Security teams struggle to identify who triggered sensitive AI actions.
Different applications implement authentication, logging, and permissions in completely different ways.

None of these problems are caused by the language model itself.

They're governance problems.

And unlike prompt engineering or model selection, governance becomes harder as your AI adoption grows.

Why Every Enterprise AI Stack Needs an AI Gateway

This is where AI gateways start becoming essential infrastructure.

Instead of allowing every application to communicate directly with every model and every MCP server, organizations introduce a centralized layer between applications and AI services.

From this:

Applications
      │
      ├── OpenAI
      ├── Anthropic
      ├── Gemini
      ├── MCP Server A
      ├── MCP Server B
      └── Internal APIs

You move to this:

Applications
      │
      ▼
 Bifrost AI Gateway
      │
      ├── LLM Providers
      ├── MCP Servers
      ├── Internal APIs
      └── Enterprise Policies

At first, adding another layer may seem unnecessary.

But that extra layer is what makes governance possible at scale.

Instead of every application implementing its own rules, the gateway becomes the single place where policies, budgets, permissions, logging, and routing decisions are applied consistently across the organization.

This architecture turns scattered AI experiments into systems teams can manage in production.

What Is Bifrost AI Gateway?

Bifrost AI Gateway is an open-source AI gateway built to centralize everything that happens between your applications and the AI services they rely on.

Instead of every application managing its own API keys, provider integrations, routing logic, MCP connections, budgets, and security policies, Bifrost provides a single control plane where those decisions are made consistently.

That includes:

Routing requests across multiple LLM providers
Managing authentication through virtual keys
Enforcing budgets and rate limits
Governing MCP server access
Logging every AI request
Applying organization-wide guardrails
Providing observability across models and tools

The result is a simpler operating model: developers build AI features while platform teams manage access, cost, and security centrally.

Bifrost acts as the central control plane for AI infrastructure, combining LLM gateway capabilities, MCP governance, observability, budgets, routing, and enterprise security into a single platform

How Bifrost Uses Virtual Keys to Secure AI Access

Managing API keys becomes more difficult as AI usage spreads across teams.

Different teams create different keys.

Applications share credentials.

Permissions become overly broad.

Lastly, nobody is entirely sure which application owns which key or what each one can access.

Bifrost approaches this differently through Virtual Keys.

Instead of exposing provider credentials directly to every application, developers authenticate against Bifrost. Behind the scenes, Bifrost handles provider credentials, while virtual keys define exactly what each application is allowed to do.

This creates a clear boundary between applications and provider credentials.

Applications no longer receive unrestricted access to models.

Instead, access becomes policy-driven.

For example, a virtual key can define:

Which models an application may use
Which providers are available
Spending limits
Rate limits
Allowed MCP servers
Tool permissions
Logging behavior

Simply, governance starts before a request ever reaches a model.

AI Governance Features in Bifrost: Guardrails, Budgets, and Audit Logs

Once AI traffic flows through a shared gateway, teams can apply the same policies across every application.

Instead of every application implementing its own security and cost controls, engineering teams can define them once and apply them across every model, provider, and MCP server.

Three capabilities matter most in production environments: guardrails, budgets, and audit logs. Together, they help organizations secure AI usage, control costs, and maintain full visibility into every request.

Guardrails

The word guardrails sometimes gets interpreted as restrictions.

Good guardrails reduce the number of decisions developers need to make manually.

Instead of asking developers to remember dozens of internal policies, guardrails make the correct behavior the default.

With Bifrost, organizations can define centralized policies that apply consistently across every AI request. Bifrost also supports integrating with different guardrails providers, allowing teams to add additional safety and compliance checks as part of their AI workflows.

Depending on the environment and the guardrails providers being used, those policies might include:

Restricting access to specific models
Blocking sensitive MCP tools
Applying content safety rules
Enforcing role-based permissions
Limiting which providers can be used
Separating development and production environments

Because these rules live inside the gateway, applications don't need to implement them individually.

Developers can continue building features while the governance layer quietly enforces organizational policies behind the scenes.

This consistency matters more as more teams start building with AI.

Budgets

Security usually receives most of the attention in AI discussions.

Cost management deserves the same attention.

Many organizations don't realize how quickly AI spending grows because usage is spread across multiple providers, applications, and internal teams.

One project might be experimenting with GPT-4.

Another relies on Claude.

A third uses Gemini for document processing.

Meanwhile, AI agents are calling MCP tools that trigger additional infrastructure costs beyond the LLM itself.

Without centralized visibility, finance teams often discover overspending after the invoice arrives.

Bifrost addresses this by making budgets part of the platform instead of an external reporting exercise.

Organizations can define:

Usage limits
Spending caps
Rate limits
Team-specific budgets
Project-specific allocations

Because every request passes through the gateway, spending can be measured consistently regardless of which provider ultimately serves the request.

This makes conversations about AI costs more productive.

Instead of asking:

"Why did our AI bill increase?"

Teams can answer:

"Which projects generated those costs, which models were used, and which requests contributed the most?"

Governance isn't about preventing overspending. It's about making spending predictable.

Audit Logs

When something unexpected happens inside a production system, the first question is usually:

"What happened?"

The problem is that this question is often difficult to answer when AI systems operate across multiple providers and dozens of applications.

Without centralized logging, reconstructing a sequence of events can require searching through application logs, cloud dashboards, provider consoles, and internal monitoring systems.

Bifrost collects those events in one place.

Requests passing through the gateway can be logged with the context engineering teams need during debugging, incident response, or compliance reviews.

That includes information such as:

Which application initiated the request
Which virtual key was used
Which user or service triggered it
Which model handled the request
Which MCP tools were accessed
Latency
Token usage
Associated costs

Instead of fragmented visibility across multiple platforms, engineering teams gain a single audit trail covering the entire AI request lifecycle.

This isn't only valuable during security incidents.

It also makes performance tuning, debugging, optimization, and compliance reporting easier.

How Bifrost Governs MCP Servers at Enterprise Scale

As organizations adopt MCP-based architectures, governance extends beyond models themselves.

Now the question becomes:

Who should be allowed to use which tools?

An MCP server might expose dozens, or even hundreds, of capabilities.

Some are harmless.

Others interact with production databases, cloud infrastructure, financial systems, or internal administrative APIs.

Giving every AI application unrestricted access simply because it connects to an MCP server isn't a sustainable approach.

Bifrost extends governance to the tool layer.

Instead of viewing an MCP server as one large permission boundary, organizations can control access more precisely, applying policies before tool execution occurs.

This allows teams to build more capable AI agents while keeping access and activity under control.

As MCP adoption grows, governance becomes a core requirement for running AI safely in production.

How Bifrost Edge Extends AI Governance to Every Endpoint

Centralizing governance at the gateway solves much of the problem.

Every AI request can be authenticated, routed, logged, budgeted, and monitored before it reaches a model or an MCP server.

But there's still one important question:

What happens before the request even reaches the gateway?

Modern engineering teams don't interact with AI through a single application anymore.

A typical developer might use:

Claude Desktop
Claude Code
Cursor
VS Code extensions
ChatGPT Desktop
Gemini
Browser-based AI assistants
Multiple MCP-enabled developer tools

Each application becomes another potential path to company data.

Even if your gateway has perfect governance, developers can still use AI applications that bypass your intended workflows if nothing exists on the endpoint itself.

This is the gap Bifrost Edge addresses.

Instead of replacing the gateway, Edge extends the same governance policies all the way to every developer machine.

It adds an endpoint layer that applies those policies where developers actually use AI tools.

Bifrost Edge extends the governance policies defined in Bifrost Gateway directly to developer endpoints, ensuring every AI application follows the same security, budget, and compliance rules

Consistent Governance Across Every AI Application

One of the biggest operational challenges for platform teams is inconsistency.

A company might carefully configure governance for one AI application while several others operate completely outside those controls.

For example:

Cursor follows company policies.
Claude Desktop uses a different configuration.
Browser AI tools bypass internal routing.
Local coding agents connect directly to external providers.
Different MCP servers expose different tool sets.

Suddenly, governance depends on which application an employee happens to open.

That's difficult to maintain, and nearly impossible to audit.

Bifrost Edge addresses this by applying the same governance policies regardless of which supported application generates the request.

Instead of configuring every AI tool individually, engineering teams can define policies once and apply them consistently across the organization.

That reduces operational overhead while improving security and compliance.

Governance That Follows the User

Traditional security often assumes that protecting servers is enough.

AI changes that assumption.

Developers now interact with company data directly from laptops, browsers, IDEs, and desktop AI applications.

That's why governance has to move closer to where AI is being used.

With Bifrost Edge, organizations can apply centralized governance to:

Desktop AI assistants
Coding agents
Browser AI tools
MCP-enabled applications
Terminal workflows

From the user's perspective, almost nothing changes.

They continue using their preferred tools.

However, behind the scenes, requests inherit the same virtual keys, guardrails, budgets, audit logs, and governance policies already configured in Bifrost Gateway.

Instead of forcing developers to adopt entirely new workflows, governance becomes largely transparent.

Good AI Governance Enables Developers Instead of Restricting Them

Effective governance enables developers to move faster because expectations become predictable.

When engineers know:

which models are approved
which MCP tools are available
which budgets apply
how requests are logged

they spend less time asking for clarification and more time building.

Instead of acting as a problem, governance becomes shared infrastructure that everyone can rely on.

This changes the role governance plays in enterprise AI systems.

The conversation is moving away from:

"Which model should we use?"

Toward:

"How do we operate AI safely across hundreds of engineers?"

How Bifrost AI Gateway and Bifrost Edge Work Together

Viewed together, Bifrost Gateway and Bifrost Edge solve two complementary problems.

Bifrost Gateway focuses on centralized AI infrastructure:

LLM routing
Provider abstraction
Virtual keys
Guardrails
Budgets
Audit logs
MCP governance
Observability

Bifrost Edge extends those same policies to the endpoint by governing:

Desktop AI applications
Browser AI assistants
Coding agents
MCP-enabled developer workflows
Individual developer machines

Instead of creating two separate systems, Edge builds directly on the governance already established in the gateway.

Final Thoughts

AI adoption is accelerating across engineering organizations. The challenge is not deciding whether to use AI but building the processes needed to manage it safely.

Guardrails, budgets, audit logs, virtual keys, and MCP governance give teams the visibility and control required to manage AI usage across models, tools, and workflows.

Bifrost AI Gateway provides centralized governance across AI infrastructure, while Bifrost Edge extends those controls to developer endpoints and everyday AI applications.

As AI becomes a bigger part of the software development lifecycle, the organizations that scale successfully won't necessarily be the ones using the most advanced models.

They'll be the ones that build the strongest operational foundation around them.

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

Top 5 AI UI Design Tools in 2026: I Tested Them All With the Same Prompt

Hadil Ben Abdallah — Mon, 06 Jul 2026 12:29:50 +0000

Looking for the best AI UI design tool in 2026? I tested Flowstep, Google Stitch, Figma Make, Lovable, and Base44 with the exact same SaaS project management prompt to compare UI quality, design consistency, code generation, developer workflow, Figma integration, and overall usability.

If you've searched for an AI UI design tool recently, you've probably noticed that every product claims it can turn a simple prompt into a polished interface in seconds. Landing pages are full of beautiful dashboards, glowing testimonials, and promises that you'll never have to start from a blank canvas again.

The problem is that those demos rarely tell you what happens when you ask the AI design tool to generate something that looks like an actual product instead of a single screenshot.

I wanted to know how these AI UI generator tools would perform on a realistic workflow.

Could they keep a design system consistent across multiple screens?
Would they generate layouts that developers could build on?
Could they produce code that was worth keeping, or would I end up rebuilding everything from scratch anyway?

Instead of trying different prompts for different tools, I decided to make things as fair as possible. I wrote one detailed prompt for a SaaS project management application and used it everywhere.

The five AI design tools I tested were:

They all approach AI-assisted UI generation differently, and after spending time with each one, it became clear that they're not really competing to solve the same problem.

If you're trying to figure out which AI UI design tool is worth adding to your workflow in 2026, here's what I learned after putting all five through the exact same test.

Why AI UI Design Tools Are Becoming Part of Every Developer's Workflow

A year or two ago, most AI UI design tools were good at generating a nice-looking landing page and not much else. Today, the landscape looks very different. Some tools can generate an entire multi-screen product, others export production-ready code, and some even build a working application from a single prompt.

That shift is changing how many developers and designers approach the early stages of product development.

Instead of spending hours creating the first version of a dashboard or wiring together placeholder screens, you can start with a solid foundation and spend your time refining the product instead of building every component from scratch.

Why I Chose a Real Product Instead of a Simple UI Prompt

Most AI UI design tools look impressive when you ask them to generate a login page or a pricing section. Those are relatively easy tasks because they're isolated screens with very little context. A beautiful first impression doesn't tell you much about how the tool performs once you're designing an actual product.

Real applications are different. They're made up of connected experiences, not standalone screens. If the design system starts drifting from one page to another, you're left cleaning up inconsistencies instead of moving faster.

I also wanted to evaluate these tools from a developer's perspective, not just a designer's. A good-looking UI is great, but it isn't the finish line. I wanted to see which tools could produce outputs that were useful in a real workflow, whether that meant exporting clean React components, fitting naturally into a Figma handoff, generating a usable design system, or even creating a working application that I could continue building instead of rebuilding.

So I wanted an answer to this question:

Which AI UI design tool is the best fit for the way you build software?

The Prompt I Used

To avoid giving any tool an unfair advantage, I used exactly the same prompt across all five platforms without changing the requirements.

I chose something much closer to what many of us build in real projects: a SaaS project management application.

I wasn't trying to trick any of the tools. I just wanted a prompt that looked like something I'd actually use if I were starting a new SaaS project.

Here's the exact prompt I used:

Design a modern SaaS project management platform for software development teams.

Generate a complete desktop application with the following screens:

1. Login
2. Dashboard
3. Projects
4. Single Project Details
5. Kanban Board
6. Sprint Planning

Requirements:

- Modern 2026 UI
- Clean spacing and typography
- Light theme
- Professional color palette
- Left sidebar navigation
- Top navigation bar
- Cards with subtle shadows
- Interactive charts on the dashboard
- Tables where appropriate
- Search bar
- Filters
- Buttons with clear hierarchy
- Empty states
- Responsive layout
- Reusable design system
- Accessible contrast
- Consistent components

Every AI UI generator tool had to generate the same six connected screens, handle the same design constraints, and solve the same UI problems.

I designed the prompt to test much more than visual quality.

How I Judged Each Tool

I didn’t look at which demo felt the most impressive at first glance. Most of these tools can generate something visually appealing in a short time, but that’s not really the hard part.

What really matters is whether the output still holds up when you zoom out and think in terms of a real product.

I evaluated every tool using the same practical criteria:

Screen coverage: Did it generate all six requested screens without dropping parts of the flow?
Design system consistency: Did typography, spacing, components, and layout stay coherent across screens, or did everything drift after the first output?
Developer usefulness: What can you do with the result? Figma file, exportable code, or just static images?
Time to usable result: How quickly did I get something I could realistically continue working with?
Workflow type
- UI generators → design frames only
- Vibe coding tools → working app output

Some tools are designed to help you design faster. Others are trying to remove the design step entirely and jump straight to a working application.

So instead of forcing them into one category, I judged each tool based on what it was trying to do, not what I personally wished it would do.

1. Flowstep

Flowstep positions itself as an AI design engineer rather than a traditional AI UI generator. That description made a lot more sense after I spent time using it. Instead of stopping at polished screens, it treats the visual canvas and the underlying code as part of the same workflow.

In practice, you start with a prompt and get back a full multi-screen interface. The interesting part is that Flowstep doesn’t stop at visual output.

What makes that possible is that Flowstep's visual canvas is built on code rather than static design layers. Instead of generating isolated mockups, it can export React, TypeScript, and Tailwind CSS, copy designs directly into Figma without plugins, meaning you can move from a generated UI to an editable design almost instantly, or send its output to coding assistants like Cursor, Claude Code, and Windsurf through MCP.

Features

Generates multiple screens in a single flow instead of one screen at a time
Simultaneous AI + manual editing of UI elements (full edit control)
Copy to Figma instantly (⌘C / ⌘V, no plugin required)
Design from references (images, URLs, or a design.md file)
React + TypeScript + Tailwind CSS code export
MCP integration for connecting AI agents and dev tools

Output

Flowstep AI-generated all screens for a SaaS project management app

Flowstep AI-generated login, dashboard & sprint screens for a SaaS project management app

A copy-pasted screen from Flowstep to Figma

What I liked

Flowstep generated the entire 6-screen flow in one pass without breaking consistency. And I noticed that it didn't think in individual screens. It immediately started building something that felt like one connected product.

It also kept:

identical sidebar structure across screens
consistent spacing system and typography scale
realistic SaaS-style data (users, projects, timestamps, issue tags, Google/GitHub-style sign-in) and dashboard-heavy interfaces with charts and operational data

Flowstep doesn’t just generate screens; it generates systems. The UI feels like it was designed with constraints. And everything is auto layout by default.

And the workflow I kept coming back to the most was the plugin-free Figma handoff. Copying a generated screen with ⌘C and pasting it directly into Figma sounds almost trivial until you compare it with tools that require exporting, importing, or rebuilding parts of the design.

During testing, I didn't find myself asking, "How do I get this into my workflow?" Instead, I was thinking about what to build next.

The speed was also noticeable. It reached a usable full-flow state faster than any other tool in the test.

Limitations

It’s still a generator, not a finished product. Even with MCP and code export, you still need engineering work to turn outputs into a fully wired application with real backend logic.

Flowstep gets you much closer to implementation, but it doesn't replace the implementation itself.

But as a starting point for designing and implementing a product, it's one of the strongest tools I tested.

2. Google Stitch

Google Stitch is about structure. It feels like Google’s attempt to solve a different part of the UI problem: instead of jumping straight into layouts, it tries to establish a design system first and then builds interfaces on top of it.

In this test, Stitch generated both the screens and a structured UI foundation alongside them, powered by Gemini models. What makes it interesting is that it doesn’t just output visual components; it also exposes the logic behind the interface: colors, typography, spacing rules, and component styles.

That design-system layer is what separates it from most other AI UI generators.

Features

Built-in design system output (colors, typography, tokens, components)
SaaS-style interface patterns
Integrated with Google ecosystem experimentation (Gemini model selection)
Automatic consistency rules derived from generated design tokens
Different export formats (AI Studio, MCP, Figma, Lovable, Netlify, Bolt, .zip)
HTML code export

Output

Google Stitch AI-generated all screens for a SaaS project management app

Google Stitch AI-generated login, dashboard & projects screens for a SaaS project management app

Google Stitch AI-generated Kinetic logic screen for a SaaS project management app

What I liked

The standout feature for me was the auto-generated design system panel.

It produced:

color tokens (primary, neutral, semantic)
typography scales
button variants
layout rules

That alone makes it valuable for system thinking.

The dashboard UI also felt “real product ready”, especially with charts and system status panels that resemble internal SaaS tools.

Limitations

It only generated 5 out of 6 screens in this test.

That sounds minor, but in real workflows it matters; missing screens break flow continuity.

Also, as an experimental Google Labs product, availability and limits can change frequently.

3. Figma Make

Figma Make has evolved beyond being just an AI feature inside Figma. It's now firmly in the vibe-coding category, allowing you to describe an application in natural language and generate a functional app directly within Figma. Instead of creating isolated mockups, it builds an interactive prototype that you can iterate on through a chat-based workflow.

One of the things that immediately stood out during testing is how transparent it is about its own decision-making. As it generates the app, it explains the design system it's creating, from grid layout and spacing to typography, colors, and component structure. That makes it much easier to understand why the interface looks the way it does.

This approach makes it especially interesting for teams that already rely heavily on Figma for collaboration, handoff, and design iteration. It doesn’t try to replace Figma; it tries to make it faster.

Features

Chat-based vibe coding directly inside Figma
Iterative refinement through conversation
Transparent design-system reasoning (grid, spacing, typography, components)
Real-time editable Figma output
TypeScript code export
Publish the app to community

Output

Figma Make AI-generated login screen for a SaaS project management app

Figma Make AI-generated dashboard screen for a SaaS project management app

Figma Make AI-generated kanban board screen for a SaaS project management app

What I liked

Figma Make produced a fully functional application with all 6 requested screens instead of just static designs. Being able to navigate through the generated app made it much easier to evaluate the overall user experience.

It also generated rich, data-heavy dashboards with sprint velocity charts, completion breakdowns, and team workload distribution.

Limitations

The only time the workflow became frustrating was when I ran into the usage model. The free tier reached its daily AI credit limit fairly quickly, which interrupted testing and made it harder to iterate on the generated app.

Generation also took longer than the other tools. That's understandable given that it's producing a functional application, but it's still something to keep in mind if you're planning to iterate rapidly.

4. Lovable

Lovable also doesn’t try to give you design files or isolated mockups. Instead, it generates a working application you can click through, complete with navigation, state, and real UI structure.

In this test, that difference became obvious very quickly. While UI generators focus on how screens look, Lovable focuses on whether the product behaves like a real product. The output feels like an early-stage SaaS you could put in front of users for feedback.

It’s also one of the clearest examples of what people now call vibe coding: you describe the app, and the tool builds something functional instead of just visual.

Features

Working navigation between screens and views
SaaS-style patterns (auth screens, dashboards, Kanban flows)
Built-in charts and analytics components for real data visualization
Share generated apps through a public link without deploying or hosting them yourself
TypeScript code export

Output

Lovable AI-generated login screen for a SaaS project management app

Lovable AI-generated dashboard screen for a SaaS project management app

Lovable AI-generated sprint planning screen for a SaaS project management app

What I liked

The first few clicks immediately gave it away. Lovable generated all 6 requested interfaces, and they weren’t just visually consistent; they behaved like actual software. Clicking through dashboards, boards, and project views felt natural, not like switching between static images.

The data modeling is also more realistic than most tools:

issue IDs like PROJ-104
priority levels (P0–P3)
real workflow states (At risk, Shipped, Blocked)

Another nice touch is that you can share the generated application with anyone through a link, even if you don't deploy or host it yourself. That makes it easy to collect feedback from teammates or stakeholders during the early stages of a project.

Limitations

It’s too slow, the slowest tool in the list.

It’s also less flexible if your goal is a clean design handoff. Unlike Flowstep or Figma Make, where you can directly work inside a design system, Lovable is oriented toward shipping a working product, not preparing design assets for a team.

5. Base44

Base44 takes a similar direction to Lovable in that it aims to generate a full working application rather than just UI screens. The difference is in how it approaches the process: it starts with a structured chat flow where it often breaks down the product into a feature plan before generating anything visually.

That planning step changes the output in subtle but noticeable ways. Instead of jumping straight into UI generation, Base44 tends to think in terms of application structure first, entities, workflows, and screen relationships. The result is usually a complete SaaS-style app shell that already feels “wired together” even before refinement.

In this test, it produced a solid project management-style application with realistic states, consistent navigation, and data-heavy screens.

Features

Chat-based app generation with structured planning phase
Full SaaS application output (not just UI screens)
Consistent entity modeling (projects, tasks, users, statuses)
Prebuilt dashboard patterns with activity and progress tracking
Built-in Kanban, sprint, and project management flows

Output

Base44 AI-generated login screen for a SaaS project management app

Base44 AI-generated reset password screen for a SaaS project management app

Base44 AI-generated dashboard screen for a SaaS project management app

What I liked

Base44 did a good job at creating a realistic default application state. The moment the app finished generating, it already felt like something a small team could start clicking through immediately. Project cards had completion percentages, due dates, and structured team assignments instead of generic placeholders.

It also maintained consistent tagging systems across screens, especially for task categorization and priority levels.

Another strong point was that it sometimes introduced useful extras that weren’t explicitly requested in the prompt, such as:

register page
forgot password page
reset password page

Limitations

While Base44 performs well structurally, its visual polish still lags slightly behind tools like Flowstep and Lovable. Spacing consistency, type hierarchy, and overall UI refinement can feel less polished in more complex screens.

Navigating between pages also felt slower than with the other tools. Base44 keeps loading every time I try to navigate to another page in the SaaS app.

Also, in the free version, you can't download or even see the app code.

Side-by-Side Comparison Table

Some tools in this list are clearly designed to generate design systems and UI layers that plug into Figma or codebases. Others are already closer to vibe-coding platforms, where the output is a working application.

That distinction is what makes this comparison useful for understanding where AI-assisted UI design is heading in 2026.

Tool	Type	Screens Generated	Generation Time	Code Export	Best For
Flowstep	UI + code export + MCP	6/6	1.5 min	React + TypeScript + Tailwind CSS	Fast, consistent multi-screen flows you can ship or hand off
Google Stitch	UI + design system generator	5/6	2.5 min	HTML	Structured design tokens and system-first UI generation
Figma Make	Vibe coding in Figma	6/6	5.5 min	TypeScript	Teams already working in Figma who want iterative AI design
Lovable	Vibe coding (full app)	6/6	10 min	TypeScript	Rapid production-ready SaaS prototypes
Base44	Vibe coding (full app)	6/6	4 min	TypeScript (Pro only)	Structured app scaffolding with realistic defaults

Looking back at all five tools side by side, I realized I'd stopped comparing visuals halfway through the experiment. Workflow ended up mattering far more than visual polish.

Which AI UI Tool Should You Actually Use?

After testing all five tools with the same prompt, one thing became clear: there isn’t a single “best” AI UI design tool in 2026. There are only tools that fit different stages of building a product.

If you try to compare them as if they all solve the same problem, the results feel confusing. But when you separate them by workflow, the decision becomes simple.

If you’re designing systems that will become real codebases → Flowstep stood out the most in this test, especially because it connects design output directly to engineering workflows through React + TypeScript + Tailwind CSS + MCP.
If you want a working product immediately → Lovable and Base44 are closer to “instant startup demo generators”.
If you live inside Figma already → Figma Make is the most natural extension of your workflow.
If your focus is system design, tokens, structure, UI rules → Stitch is a good choice.

Final Thoughts

When I started this comparison, I expected to spend most of my time judging layouts, typography, and visual polish. Instead, I found myself paying much more attention to something else: how each tool fits into the way people build software.

They’re splitting into two categories:

design systems that think in structure (Flowstep, Stitch)
tools that already behave like app builders (Figma Make, Lovable, Base44)

Neither category is inherently better; they simply solve different problems. That was the biggest takeaway from this experiment.

Whether you're building the frontend yourself, collaborating with a designer, or shipping an entire SaaS product, choosing the right tool is more about finding the one that fits naturally into the way you already build software.

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

Where Do Your LLM API Keys Actually Live?

Hadil Ben Abdallah — Mon, 06 Jul 2026 08:32:55 +0000

If someone compromised one of your project's dependencies today, would they be able to steal your OpenAI, Anthropic, or Gemini API keys?

The answer isn't based on which LLM provider you use or how secure your codebase is. It mostly depends on one architectural decision that many teams never think about: where your provider API key actually lives while your application is running.

If that key lives inside your application's own process, every dependency running in that process shares the same environment. A compromised package doesn't need to break into your infrastructure. It simply executes with the same privileges as your application and can access the same credentials your code can access.

If the provider key lives in a separate network proxy instead, the application never holds the provider credential at all. Even if a dependency is compromised, the attacker can only access whatever limited credentials exist inside the application process. That doesn't eliminate risk, but it can reduce the blast radius when something goes wrong.

In this article, we'll look at the two dominant LLM gateway architectures, examine exactly where API keys live in each design, walk through a reproducible demo that shows the difference in practice, and discuss why reducing blast radius often matters more than trying to eliminate every possible attack.

LLM Gateway Architectures: In-Process vs Network Proxy

Every LLM application has the same fundamental job to do: send a request to a model provider and authenticate that request using an API key.

The important part isn't whether an application uses an API key. It's where that key exists while the request is being made.

Today, most AI applications follow one of two architectural patterns.

Architecture 1: The application holds the provider key

The provider key lives inside the app process, in reach of any dependency that runs at import

This is the architecture most developers are already familiar with.

Your application loads the provider API key from an environment variable, initializes an SDK or gateway library, and sends requests directly to OpenAI, Anthropic, Gemini, or another provider.

A simplified version often looks like this:

api_key = os.environ["PROVIDER_API_KEY"]

client = OpenAI(api_key=api_key)

response = client.responses.create(...)

It's easy to understand, quick to implement, and perfectly reasonable for many projects. The application owns the credential because it's the component talking directly to the provider.

The important detail is that the provider key now lives inside the application's process. Every package, framework, plugin, and dependency that executes in that process runs under the same privileges. If one of those dependencies is compromised, the provider key exists in the same environment as the malicious code.

The architectural question is this:

If something inside the application process is compromised, what secrets are available from there?

Architecture 2: The application talks to a network proxy

The provider key lives in a separate proxy process. The app process holds only a scoped, rotatable gateway token

The second pattern separates authentication from the application itself.

Instead of sending requests directly to the model provider, the application sends them to a gateway or proxy. The proxy owns the provider API key and performs the upstream request on the application's behalf.

From the application's perspective, the flow looks almost identical:

Application
        │
        ▼
Gateway / Proxy
        │
        ▼
LLM Provider

The difference is what the application doesn't have.

Rather than storing the provider credential, it typically holds a scoped gateway token that authorizes requests through the proxy. The proxy validates that token, applies any routing or policy decisions, and then injects the provider API key only inside its own process before forwarding the request upstream.

This changes the consequences of a compromise. If malicious code executes inside the application process, it can still access whatever credentials the application possesses. The difference is that the provider API key is no longer one of them.

That doesn't make the application invulnerable. A stolen gateway token is still a security incident. However, unlike a provider API key, a gateway token can be narrowly scoped, centrally revoked, rotated without redeploying applications, and restricted to specific operations.

The easiest way to see that difference is by watching the exact same compromised dependency run against both architectures. That's what we'll do next.

How Supply Chain Attacks Expose LLM API Keys

The more important question is what happens after your application starts running.

Once a process begins executing, the credentials it needs become available to that process. If your application can read an API key, any code executing with the same privileges can potentially read it too.

That is exactly why supply chain attacks have become so effective.

An attacker no longer needs to find a vulnerability in your application. Instead, they compromise a package somewhere in your dependency tree and let your application execute the payload on their behalf. In many cases, that code runs during installation or import, long before your own business logic starts.

import os

_INTERESTING = ("API_KEY", "SECRET", "TOKEN", "PASSWORD", "PRIVATE_KEY")

def harvest():
    found = {
        k: v
        for k, v in os.environ.items()
        if any(marker in k.upper() for marker in _INTERESTING)
    }

    for name, value in found.items():
        print(f"Found: {name}")

harvest()

This example is deliberately harmless. It doesn't make network requests, write files, or attempt to exfiltrate anything. It simply scans the current process for credentials and prints what it finds.

The important part isn't what the code does. It's where the code runs.

Imagine this package sits several layers deep in your dependency graph. You don't import it directly, and you've never read its source code. One day, a compromised release reaches your CI pipeline, gets installed automatically, and executes as part of the normal startup sequence.

If your application stores a provider API key in its own environment, the dependency can read that key because it exists in the same process.

If your application instead holds only a scoped gateway token while the provider credential lives inside a separate proxy process, the exact same dependency still executes successfully, but the provider key simply isn't there to discover.

That's the architectural distinction we're exploring.

It's also why the March 2026 LiteLLM supply chain incident attracted so much attention across the AI ecosystem. The incident wasn't important because LiteLLM was uniquely vulnerable. It was important because it demonstrated how valuable AI infrastructure has become as a target and how quickly a compromised dependency can reach high-value credentials inside running applications.

Before looking at that real-world case, it's worth seeing the difference.

The following reproducible demo runs the same compromised dependency against both architectures. Nothing about the dependency changes. The only variable is where the provider API key lives.

Demo: In-Process vs Proxy LLM Gateway Security

Theory is useful, but it's much easier to understand architectural risk when you can see it happen.

To make this comparison concrete, I put together a small, dependency-free demo (provided by Jonathan Hutchins for this article) that recreates the exact same scenario against both architectures.

The setup is intentionally simple:

The application itself never changes.
The same dependency is imported in both examples.
The only thing that changes is where the provider API key lives.

The demo uses only Python's standard library. There are no external services, no provider accounts, no network calls to OpenAI or Anthropic, and no real credentials. Everything runs locally, making it easy to reproduce without worrying about side effects.

The "malicious" dependency is equally straightforward. When it's imported, it scans the current process for anything that looks like a credential.

import os

_INTERESTING = (
    "API_KEY",
    "SECRET",
    "TOKEN",
    "PASSWORD",
    "PRIVATE_KEY",
)

def harvest():
    found = {
        k: v
        for k, v in os.environ.items()
        if any(marker in k.upper() for marker in _INTERESTING)
    }

    for name, value in found.items():
        shown = value[:8] + "..." if len(value) > 12 else value
        print(f"EXFILTRATED {name} = {shown}")

harvest()

Scenario A: The provider key lives inside the application

The first version follows the architecture many AI applications use today.

The application reads the provider key from its own environment:

api_key = os.environ["PROVIDER_API_KEY"]

When the dependency is imported, it runs inside exactly the same process.

As a result, it immediately discovers the provider credential:

[malicious_dep@import]
EXFILTRATED PROVIDER_API_KEY = sk-provi...3xyz

Nothing particularly clever happened here.

The dependency didn't bypass authentication, exploit memory corruption, or break into another service. It simply accessed data that already existed in the process it was executing inside.

From the attacker's perspective, that's enough.

Scenario B: The provider key lives inside a network proxy

Now let's run the exact same dependency against the second architecture.

This time, the application never receives the provider credential.

Instead, it holds only a gateway token:

token = os.environ["GATEWAY_TOKEN"]

The provider API key exists only inside the proxy process, which validates the gateway token before forwarding requests upstream.

When the compromised dependency runs, the output looks very different:

[malicious_dep@import]
EXFILTRATED GATEWAY_TOKEN = gw-scope...-789

Notice what didn't appear.

There is no provider API key because it never existed inside the application's process in the first place.

The application still receives a successful model response, but the authentication to the LLM provider happens inside the proxy rather than inside the application itself.

At this point, it's tempting to conclude that the proxy "solves" the problem.

It doesn't.

The dependency still stole a credential. The gateway token is real, and if an attacker gets hold of it, they may still be able to make requests through the proxy. Pretending otherwise would make this comparison less useful.

The question isn't whether something leaked. It's what leaked, what that credential can do, and how quickly you can recover from its exposure.

That's where the two architectures begin to diverge in a much more meaningful way.

The next part of the demo shows exactly what happens after a gateway token has already been stolen and why recovery looks very different from rotating a compromised provider API key.

What a Proxy Protects and What It Doesn't

In the previous example, the compromised dependency still stole a credential.

It just wasn't the provider API key.

Instead, it obtained a scoped gateway token that allows requests through the proxy. That's still a security incident, and it's important to acknowledge that upfront. Security discussions become more useful when they describe trade-offs.

The interesting part comes after the compromise. The demo's rotate_demo.sh script walks through the recovery process step by step.

Initially, both the legitimate application and the attacker possess the same gateway token, so both can use it successfully. This temporary overlap is expected until the operator revokes the compromised credential.

Then the operator updates the proxy's token store.

The original token is revoked.

A new scoped token is issued.

Nothing about the application code changes.

Nothing is redeployed.

Nothing is restarted.

The proxy simply begins rejecting the compromised credential while accepting the replacement.

The result looks like this:

STEP 4  After rotation

[attacker (stolen v1)] BLOCKED -> HTTP 401
[app (v2)] ACCEPTED -> completion ok

The final part of the demo shows another important property of gateway tokens: scope.

Instead of representing unrestricted access to an LLM provider account, the token is valid only for the operations it was explicitly created to perform.

If that same token is presented outside its permitted scope, the proxy rejects it.

STEP 5  Scoping

[app (v2, wrong scope)] BLOCKED -> HTTP 403

A provider API key is typically long-lived and grants direct access to your provider account. If it's compromised, rotating it often means updating secrets across multiple services, redeploying applications, and carefully coordinating the change to avoid downtime.

A gateway token represents something much smaller. It can be scoped to a single application, route, team, or temporary workload. If it leaks, the operator can revoke it centrally, issue a replacement, and continue operating without touching the provider credential itself. That doesn't make the compromise harmless, but it makes recovery simpler.

This distinction is becoming increasingly relevant as AI systems grow more complex, with agentic workflows depending on many libraries, plugins, orchestration frameworks, and MCP servers. Each additional component expands the trusted computing base, making blast-radius reduction as important as preventing failures entirely.

Of course, this isn't just a theoretical discussion. In March 2026, the AI ecosystem watched a real supply chain compromise unfold that illustrated exactly why the location of your credentials matters. Rather than asking developers to imagine the risk, it provided a real-world example of how quickly a compromised dependency can turn into a much larger security incident. That's the incident we'll examine next.

The March 2026 LiteLLM Supply Chain Attack Explained

In March 2026, LiteLLM, one of the most widely used gateways for interacting with multiple LLM providers, became part of a larger software supply chain campaign that affected several open-source projects.

According to LiteLLM's own security postmortem, attackers were able to publish two compromised package versions (1.82.7 and 1.82.8) to PyPI after stealing a publishing token from the project's CI pipeline. The compromise itself originated upstream through a malicious GitHub Action rather than a vulnerability in LiteLLM's application code, a detail also documented by Datadog Security Labs and FutureSearch.

LiteLLM's postmortem estimates the malicious releases were available for about 40 minutes, while independent analyses place the window closer to three hours. Either way, it was enough time for automated CI pipelines to install compromised packages.

The malicious releases searched for high-value credentials, including cloud secrets, SSH keys, Kubernetes tokens, database credentials, and API keys, before attempting to exfiltrate them. LiteLLM's postmortem provides a detailed list of the affected credential types, while Datadog Security Labs analyzed how the payload operated once executed.

One of the most publicized downstream victims was Mercor, which later confirmed a security incident tied to the compromised packages. The case illustrated how a compromise in a widely used dependency can quickly propagate across organizations that never directly interacted with the original attackers.

The takeaway isn't that LiteLLM was uniquely risky. The compromise originated from a malicious GitHub Action rather than LiteLLM's application code, and the project responded quickly by publishing a postmortem, rebuilding its release pipeline, and releasing a clean version (v1.83.0). The official LiteLLM Proxy Docker deployment, which pins dependencies, was also unaffected, reinforcing the value of dependency pinning, lockfiles, and verified builds.

The biggest lesson was about architecture.

LLM gateways occupy a uniquely sensitive position because they manage credentials that unlock access to multiple providers. Wherever those credentials live becomes an attractive target during a compromise.

That's why the question isn't "Could one of my dependencies become compromised?"

It's this:

If that happens tomorrow, what credentials would the attacker find inside my application's process?

So, Where Should You Store LLM API Keys?

The answer isn't "always behind a proxy" or "always inside your application".

The right architecture depends on your team's operational needs, deployment model, performance requirements, and security priorities.

What this article hopefully makes clear is that where your provider API key lives directly determines the consequences of a compromise.

If your application holds the provider key, any code executing with the application's privileges can potentially access it. That doesn't automatically make the architecture insecure. Plenty of production systems successfully use in-process libraries alongside pinned dependencies, lockfiles, isolated CI/CD pipelines, secret managers, and strict network controls.

If your application instead talks to a network proxy, the provider key moves into a separate process. The application typically holds only a scoped gateway token. If malicious code executes inside the application, the attacker can still steal that token, but the provider account itself remains outside the application's blast radius. Recovery becomes a matter of revoking and rotating a scoped credential instead of replacing a provider key across every service that depends on it.

Neither architecture eliminates the need for dependency pinning, reproducible builds, CI/CD hardening, least-privilege access, and continuous monitoring. Those practices remain essential regardless of where your API keys live. Architecture simply determines what an attacker can reach if those defenses fail.

If you're evaluating your own architecture, a few practical questions can help guide the discussion:

Where does the provider API key exist while my application is running?
Which processes can access that credential?
If one dependency in my application became compromised today, what secrets could it reach?
Can those credentials be scoped, revoked, and rotated independently of the provider account?
How long would recovery take after a credential leak?

And if you're considering a proxy-based architecture, there are several implementations available today, including the official LiteLLM Proxy deployment, cloud API gateways placed in front of model providers, and proxy-native solutions such as SteadIO.

SteadIO is an open-source self-hosted LLM gateway that sits between your application and model providers like OpenAI and Anthropic. Besides isolating provider API keys from the application process, it also adds several operational capabilities that become increasingly valuable as AI systems grow.

Key capabilities include:

Per-agent and per-team request attribution, making it easy to understand which agents are generating traffic.
Real-time token and cost tracking using provider-accurate pricing.
Budget enforcement, allowing teams to stop runaway agents automatically before costs spiral out of control.
Centralized authentication and gateway token management, so scoped credentials can be issued, revoked, and rotated without changing the provider API key.
A single control plane for monitoring AI traffic, since every request already passes through the gateway.

Regardless of the gateway you choose, the key architectural advantage is reducing the blast radius by keeping provider credentials outside the application process.

Example dashboard from SteadIO showing centralized cost attribution and agent-level AI spending

Ultimately, the question isn't really about API keys. It's about designing systems that fail gracefully.

Because no matter how mature your security program becomes, vulnerabilities will appear, dependencies will be compromised, and mistakes will happen. When that day comes, the most valuable security decision may not be the one that prevented the incident.

It may be the architectural decision that kept the blast radius small enough to recover quickly.

Final Thoughts

No architecture can prevent every supply chain attack or compromised dependency. What you can control is which credentials are exposed when something goes wrong and how quickly you can recover.

So before shipping your next AI application, take a moment to answer the question we started with:

Where do your LLM API keys actually live?

The answer may have a greater impact on your security posture than the model provider or SDK you choose.

This article was co-authored by Jonathan Hutchins, Founder of SteadIO, whose technical insights and demo helped shape many of the architectural concepts explored throughout this article.

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