Vibe Coding in 2026: What Happens After the First Week

A guide to AI app builders, AI coding editors, and the inevitable ecosystem of services that grows around them

Vibe coding is genuinely a new way to build products. The term emerged in February 2025, and in just a year and a half it has grown into an industry generating hundreds of millions of dollars in revenue. Lovable alone reported $500M in ARR. Cursor became the second most popular editor among developers, while GitHub Copilot is now integrated into nearly every second repository on GitHub.

The first week with almost any vibe-coding tool is usually exciting. You can build an MVP in an hour and a half, a working online store in an evening, or a SaaS product with subscriptions and payments over a weekend.

From the second week onward, however, the problems begin to surface and become more noticeable with each passing week. As they accumulate, one question becomes increasingly relevant — a question that rarely appears in marketing materials: what is everything else built from once it can no longer be generated with a single prompt?

That is exactly what this article is about.

AI App Builders: What They Really Do Well

One of the most prominent categories of vibe-coding tools today is AI app builders. The idea behind them is straightforward: the user describes a product in natural language, and the system turns that description into a working frontend with basic logic.

Their biggest advantage is speed. Where building an initial MVP once required weeks of work and a budget of $3,000–5,000, it is now possible to produce a working prototype in 30–90 minutes. And it is not just something that looks like a product; it is functional enough to test the idea in practice.

By 2026, this market had already expanded significantly, with tools developing different areas of specialization. Lovable has become one of the most popular options and can generate complete Next.js projects with Supabase as the backend. Bolt.new by StackBlitz runs directly in the browser through WebContainers and can spin up a project in seconds. v0 by Vercel is particularly strong at generating React and Next.js components and fits naturally into the Vercel ecosystem.

Replit Agent offers a fully cloud-based workflow that includes deployment, a database, authentication, and file handling alongside the code itself. Base44 by Wix is focused primarily on internal tools and admin panels. Manus operates as an AI agent for generating full-stack applications, Softgen focuses on Next.js, and Rork on React Native mobile apps. Create.xyz / Anything follows the model of a closed full-stack builder, while Figma Make can generate code directly from a Figma design.

Despite their differences, all of these tools share one major strength: they are especially effective when the goal is to turn an idea into something tangible as quickly as possible. They reduce the path from concept to a working result from weeks to hours. And that is exactly what they were built to do.

AI Code Editors: How They Are Changing Development

The second major category of vibe-coding tools is AI integrated directly into the development environment. Here, developers still write the code themselves, but AI takes over a significant share of the supporting work: suggesting and completing code, refactoring, helping identify errors, and preparing documentation.

For a professional developer working with a familiar stack, this can increase development speed by 3–5x.

The main tools on the market in 2026 include:

• Cursor — effectively the de facto standard among AI code editors, with particularly strong context handling.
• Claude Code (Anthropic) — a CLI and IDE plugins with a deep understanding of code architecture.
• Windsurf / Devin Desktop (Cognition) — includes the autonomous Cascade agent.
• GitHub Copilot (Microsoft) — has the largest user base and is tightly integrated with GitHub.
• Devin (cloud) — an autonomous agent that operates in its own cloud sandboxes.
• Cline — an open-source extension for VS Code.
• Aider — an open-source CLI tool for the terminal.
• Amazon Q Developer — deeply integrated into the AWS ecosystem.

These tools are particularly effective in production development, including refactoring, generating supporting code, testing, debugging, and preparing documentation.

They do not replace developers, but they can significantly accelerate their work.

What Everything Else Is Built From

Both categories of AI tools share the same limitation: they primarily work with code. But a real product is not limited to frontend and business logic.

Behind them sit data, content, payments, files, email, analytics, search, real-time functionality, background jobs, error monitoring, infrastructure, and many other components. And each of these components also has to be implemented somewhere.

In practice, it looks like this:

Almost every layer has several alternatives. And each comes with its own API, documentation, operating logic, pricing model, and update cycle.

As a result, a developer’s job is no longer just to write code. They also need to choose the right service for each layer, connect those services correctly, and then keep the entire system working over time.

What This Looks Like in Real-World Projects

In practice, a stack rarely stops at two or three external services. As long as the product remains simple, that may be enough. But as soon as new requirements appear, the number of tools that need to be connected starts to grow.

Stack A. Minimal SaaS — 5 services

Lovable + Supabase + Stripe + Resend + Vercel

For example, a solo founder launches a habit-tracking SaaS. Lovable handles the frontend, Supabase the database and Auth, Stripe the subscription, Resend the welcome email, and Vercel the hosting.

This is close to the minimum viable setup. In most cases, it is difficult to reduce the stack much further.

Stack B. Indie developer with a production SaaS — 9 services

Cursor + Supabase + Stripe + Clerk + Cloudinary + Resend + PostHog + Sentry + Vercel

Once the product moves beyond the first MVP, the stack begins to grow. A solo founder adds analytics, a separate Auth service because Supabase Auth is no longer sufficient, error monitoring, and image optimization.

The result is already a fully functional production SaaS. A significant share of real-world projects remain at roughly this level.

Stack C. Small business: localized online store — 12 services

Lovable + Supabase + Stripe + Midtrans + Resend + Cloudinary + Sanity + Crisp + PostHog + Algolia + Retool + Vercel

When the product serves a real business, the requirements become broader. For example, the owner of a ceramics studio may need more than just an online store: local payment methods, a blog, customer support chat, analytics, search, and a separate admin panel for an assistant.

Each new business requirement adds another service to the stack. As a result, the number of tools grows almost in proportion to the complexity of the product itself.

Stack D. E-commerce on headless Shopify — 8 services

Bolt + Shopify Storefront API + Sanity + Cloudinary + Klaviyo + PostHog + Sentry + Vercel

In another scenario, a brand uses Shopify as the backend but builds the frontend independently to achieve a unique design. Shopify handles products and payments, Sanity the blog, Klaviyo email marketing, while the remaining responsibilities are distributed across additional services.

This is a common setup for brand-first stores.

Across all of these examples, the same pattern repeats: a real product quickly grows into a stack of 5–12 services. None of them covers the product end to end, and all of the connections between them remain the developer’s responsibility.

Limitations of the Services Themselves

The problem is not limited to integration complexity. As long as the product remains simple, many limitations of individual services are barely noticeable. But as the product evolves into a full-fledged business tool, those limitations become increasingly apparent.

1. Data models are not built for e-commerce

Supabase, Firebase, and Neon provide developers with a database, but not with a ready-made e-commerce data model.

Entities such as orders, order_items, discounts, coupons, subscriptions, refunds, bonus_transactions, and loyalty_tiers still have to be designed from scratch.

As a result, every founder ends up solving many of the same problems again. And business rules quickly become more complex. Even a condition like “a coupon cannot be combined with a seasonal discount unless the order total exceeds $50” can eventually turn into dozens or even hundreds of lines of business logic.

2. There is no production-ready admin panel for non-technical staff

Supabase Studio and Firebase Console are designed primarily for developers.

Giving a content manager or operator access to them can be risky, because they may gain access not only to the data they need, but also to internal system data.

That is when another tool, such as Retool or Forest Admin, gets added to the stack.

It is possible to generate an admin panel with Lovable, but then roles, audit logs, version history, and other production requirements still have to be handled separately.

3. Multilingual support remains DIY

There is no unified approach to i18n across this kind of stack.

In Supabase, translations may be stored in separate columns or translation tables. Sanity has its own configuration. In Stripe, locale is set through the Checkout Session. In Resend, separate email templates have to be maintained.

So adding a second language is rarely a matter of changing a single setting. It affects the data structure, UI strings, CMS, email templates, and part of the business logic.

As a result, localization can easily turn into a separate project lasting several weeks.

4. A mobile app effectively creates a second backend scenario

The issue is not mobile itself, but the fact that APIs and integrations often behave differently from web.

Web OAuth does not work the same way in a mobile app, so a native SDK is required. Stripe Checkout does not work inside an app in the same way it does in a browser and requires PaymentIntents, Apple Pay, or Google Pay. Push notifications introduce FCM or APNs and separate subscription logic. Sessions between web and mobile also need to be synchronized.

So a mobile version is usually not simply a port of an existing web product. In practice, it becomes a second product that uses the same database but requires its own integration logic.

5. Roles and permissions are rarely granular enough

Most BaaS platforms provide basic access levels such as admin and authenticated user.

For a real business, that is usually not enough.

You may need:

• a content manager to see only products;
• a support operator to work only with orders;
• a marketer to access only discounts and campaigns;
• a finance employee to see only transactions.

This kind of granular RBAC has to be built manually using Row Level Security in Postgres, custom claims in Firebase, or custom middleware.

6. Bulk operations remain a secondary use case

As long as the system works with small amounts of data, this is barely noticeable. But once thousands of products or records are involved, the limitations become obvious.

Importing 5,000 products through the Airtable API runs into rate limits, batching, and long processing times.

With Supabase, developers have to implement bulk inserts and monitor memory usage.

Sanity provides a CLI with a progress bar, but request limits still remain.

None of these tools was originally designed around bulk operations as a first-class use case. As a result, queues, batching, retry logic, and error handling once again become the developer’s responsibility.

The Final Cost of the “Service Zoo”

As the stack grows, its cost is no longer measured only by the number of subscriptions. It begins to show up in three different ways: money, time, and hidden costs that often become apparent only once the product is already in production.

A. Money

If we take the stacks from the previous section, the approximate monthly costs look like this:

• Stack A, 5 services: $50–100 per month;
• Stack B, 9 services: $150–300 per month;
• Stack C, 12 services: $300–700 per month;
• Stack D, headless Shopify, 8 services: $500–1,500 per month.

And these are only the base costs.

Most of these services use usage-based billing, which means the final bill depends directly on load. Firebase charges for reads. Supabase charges for bandwidth and compute. PostHog charges for events. Cloudinary charges for transformations and bandwidth. Sanity charges for API calls.

As traffic grows, costs therefore increase across several services at the same time. And that growth is far from always linear.

B. Time

In practice, time often becomes a more expensive resource than the subscriptions themselves.

On average:

• setting up a new service from scratch takes 2–8 hours;
• learning a third-party SDK and its specifics takes 4–16 hours;
• a single breaking API change can cost 1–3 days of work;
• a new business feature that touches 5–7 services can require 2–5 days of integration work, even if AI generated the code itself in an hour.

As a result, in a project built around a large number of external services, a significant share of development time is no longer spent on improving the product itself, but on maintaining the connections between its individual components.

In some cases, this can account for 50–70% of a developer’s working time.

C. Hidden Costs

There are also costs that are much harder to see on an invoice, yet often become the most painful over time.

Vendor lock-in. Migrating from Firebase to Postgres can take weeks. Replacing Sanity with another CMS means rewriting frontend queries and reworking the content. Moving from Stripe to another payment processor means migrating saved cards and subscriptions.

System fragility. If a product depends on twelve external services, there are twelve independent points in the chain where something can stop working.

Cognitive load. A new developer joining the project is no longer learning only the codebase. They also have to understand the surrounding infrastructure, the connections between services, and the rules governing how those services interact.

In the end, the cost of the “service zoo” is not limited to monthly subscription fees. It gradually shifts into maintenance, integrations, migrations, team onboarding, and the constant effort required to keep the entire system running.

Conclusion

Vibe coding genuinely works. Lovable and Bolt can build a working frontend in an hour, while Cursor and Claude Code can significantly accelerate professional development. This is no longer an experiment or short-term hype.

AI can write a function in 15 minutes, but a real product consists of much more than functions. Data, payments, emails, files, content, analytics, search, localization, mobile, admin tools, and other layers are still distributed across 8–13 separate SaaS services.

Each of them:

• has its own data model;
• uses its own SDK and terminology;
• updates independently and can break existing integrations;
• knows nothing about the services around it;
• has functional limitations that the team must address manually.

Every month, smarter AI builders appear. Every quarter, more autonomous AI agents are released. But the infrastructure layer beneath them barely changes. The same zoo of 8–13 services, connected through a web of separate integrations, remains in place.

So the key question is no longer how quickly AI can write code. The real question is what needs to change beneath that code so that the promised shipping velocity stops being an effect of the first two weeks and can be sustained as the product grows.

And for now, in our view, the industry still does not treat this as a distinct systemic challenge. But without solving it, even the smartest AI agent of 2027 may generate code faster and faster, while a complete product will still take weeks to assemble.