The Hidden Bottleneck in AI Development (and How We Removed It)

Hey everyone, I’m Daniel (intro post here), DevRel at Xano.

Three years ago, I shipped my first mobile app in 30 days using no-code tools. A few months ago, I shipped SessionBridge—an AI memory platform that lets you take your session memory with you as you move across Claude, ChatGPT, and Cursor—in 3 days.

The difference? Two things: (1) AI can now generate a working frontend incredibly fast. (2) I stopped treating the backend like something I needed to build from scratch.

The Problem Every AI Builder Hits

If you're leveraging AI to develop software, you know the tension:

AI coding tools are incredibly fast. You can generate a working prototype in minutes, but real software is more complex than that. Tools like Cursor, Lovable, and Claude Code feel like magic—until you need to actually ship it.

Building a production-grade backend is incredibly slow. Setting up auth, database schemas, API endpoints, scaling infrastructure, security, devops... suddenly your "ship in a weekend" project becomes "ship in a quarter." This is probably why many of the big vibe coders are rushing to integrate their own backend. They recognize that this is where the bottleneck lies. But, we can’t expect them to achieve overnight what has taken years for true production-grade backend tools and platforms to build.

You're stuck choosing between:

• Fast but fragile: AI-generated backends that work in demos but break in production
• Slow but solid: Traditional development where you spend weeks on infrastructure before building features

There's a third option most developers don't know about.

Production-Grade Backend in Minutes

A few years ago, after a successful career as a senior engineer at Picasa and later at Google—where he played a key role in building and scaling what became Google Photos—Sean Montgomery left to co-found a software development agency with a friend.

He and his co-founder quickly noticed a pattern.

While every project was unique, they kept building the same backends for different clients. Auth systems. Database schemas. API endpoints. Payment processing. File storage. The same patterns, over and over. The same proven stack, Postgresql, Docker, Kubernetes, and Redis, over and over.

So they built an internal tool that let them spin up production-grade backends in days instead of months. That internal tool evolved into Xano—a no-code backend platform with zero devops required.

Here's what makes it interesting for AI builders: Xano was designed to be fast enough for prototypes and scalable enough for production. No switching costs. No rewrites. You ship fast, and it scales.

Companies like Deriv (an online trading broker with over 3 million customers worldwide) use Xano to ship features faster. Non-technical builders use it to launch products without hiring engineers. And now, with Xano 2.0, developers can build backends using AI in their IDE while maintaining production-grade infrastructure.

Xano Evolves to Embrace Code & AI Building

Xano was always programmable via a Metadata API and you could build complex backend logic using a visual function builder. It worked, but some pro developers still wanted the benefits of Xano while writing code in their IDE.

The latest version of Xano (release 2.0), which was just announced this week, introduces three things that change what's possible:

1. XanoScript: Your Backend as Actual Code

XanoScript is a scripting language that lets you build Xano backends using code instead of visual blocks.

Here's what makes it different: Changes you make in code are reflected in the visual interface, and vice versa. Your backend exists as both code and visual representation simultaneously.

Why this matters:

• Pro developers can write code in their IDE and work at full speed
• Product owners can view the same logic visually to understand what's happening
• AI tools can generate and easily modify your backend because it's code, not configuration
• Teams can collaborate without forcing everyone into the same modality

No other backend platform does round-trip code ↔ visual like this. Once you go to code in other platforms, you lose the visual layer. With Xano, you keep both.

2. Xano MCP Server: Build, Audit, and Chat With Your Backend From Anywhere

The Xano MCP (Model Context Protocol) Server lets you build your entire Xano backend from outside Xano.

This means:

• Use Claude Code, Cursor, and other any MCP-compatable agent to interact with or gain context of your backend
• Your AI coding assistant can actually see and modify your backend structure, data, and logs
• Write XanoScript in VS Code, push changes directly to Xano
• Version control your backend logic alongside your frontend code

The unlock: Your AI coding assistant finally has context about your backend. It's not just generating code you need to manually implement—it's generating code that becomes your actual backend.

(Creating Workflow Tests via Xano MCP)

3. VS Code Extension: Native IDE Integration

The Xano VS Code extension brings your backend into your development environment.

You can:

• Browse and edit Xano resources directly in VS Code
• Write XanoScript with full IDE support (autocomplete, linting, etc.)
• Push and pull changes between your local environment and Xano
• Use AI tools inside VS Code to generate backend logic with full context
• Package and share backend templates via GitHub

This is what makes Xano competitive with "vibe coding" tools like Lovable and Bolt. You get the AI speed, but your backend is production-grade from day one.

Why This Matters for AI Builders

When I built SessionBridge, I needed:

• User authentication
• Database schema for conversations, artifacts, and memory
• API endpoints for the app
• An MCP for data ingestion and smart context retrieval
• Hybrid RAG and Full Text Search functionality wrapped in an MCP tool
• File uploads and batch processing
• Background tasks for memory indexing
• Plus a whole lot more

This would have taken much longer on another platform even with the help of AI. I wouldn't have the same guardrails and speed.

With Xano, I focused on the logic and shipped in 3 days. The backend just worked.

But here's what's new with 2.0: I could have used AI to generate most of that backend logic directly. Instead of building functions visually or writing them in Xano's interface, I could have described what I needed and had AI generate the XanoScript.

The pattern looks like this:

1. Describe your backend to AI: "Create a backend for MintLab (a mintlify clone) - a multi-tenant documentation platform where teams host versioned MDX docs with search, analytics, and collaboration."

2. AI generates XanoScript: Your coding agent writes the actual backend code using XanoScript

(The video was too large to post here so I uploaded it to YouTube here)

Here’s the summary:

I've successfully implemented the complete MintLab backend according to the plan. Here's what has been built:

✅ Database Schema (6 Tables)

• team - Core tenant entities with unique slugs
• team_member - Membership with role hierarchy (owner/admin/member)
• documentation_site - Projects within teams
• doc_version - Version management for documentation
• doc_page - MDX content pages with hierarchical structure
• page_view - Analytics tracking for visitors ### ✅ API Endpoints (27 Total) Teams API (7 endpoints)
• Team CRUD operations
• Member management (add, remove, update roles)
• Role-based permissions (owner, admin, member) Documentation API (17 endpoints)
• Site management (create, list, update, delete)
• Version management (create, list, update, delete)
• Page management (CRUD with MDX support)
• Search functionality with relevance scoring
• Public and authenticated endpoints Analytics API (3 endpoints)
• Page view tracking (public)
• Page-level analytics
• Site-wide analytics with date filtering ### ✅ Helper Functions (4 Total)
• check_team_permission - Permission verification with role hierarchy
• check_site_permission - Site access via team membership
• search_pages_helper - Full-text search with scoring
• calculate_page_analytics - Page view aggregation
• calculate_site_analytics - Site-wide metrics ### Key Features Implemented
• Multi-tenancy: Team-based isolation with proper access control
• Versioning: Multiple doc versions per site with publish states
• Content Management: MDX support with hierarchical pages
• Search: Full-text search across title and content
• Analytics: Anonymous + authenticated tracking with metrics
• Security: Role-based permissions, JWT authentication ### No Linter Errors All code has been validated and passes linting checks. The implementation follows XanoScript best practices and syntax guidelines. ### Next Step You can now push all changes to your Xano backend using the appropriate tools. The backend is ready for frontend development and testing!

1. Stage, Push, and Diff in One Click: Stage and Commit changes

Validate or Review Visually: Product owners or designers can toggle to visual view to understand the logic without reading code and even build.

Iterate fast: Make changes in code or visual, both stay in sync, while leaving the devops and infrastructure to us.

This is the production-grade alternative to tools like Lovable and Bolt. You get AI speed with enterprise infrastructure, security, and scalability baked in.

The Infrastructure You Don't Have to Build

Here's what Xano handles so you don't have to:

Infrastructure: Auto-scaling, load balancing, global CDN, 99.9% uptime SLA

Security: SOC 2 Type II certified, GDPR compliant, encryption at rest and in transit

Features: Built-in auth, file storage, background tasks, webhooks, real-time data, caching

Devops: Zero. You write backend logic. Xano handles everything else.

This is why companies with large engineering teams use Xano. It's not about "can our developers build this?"—it's about "should our developers spend time on undifferentiated infrastructure?"

Deriv has over 100 developers. They chose Xano because it lets them ship features faster without managing backend infrastructure.

What You Can Build Now

With XanoScript + MCP + VS Code integration, you can:

Ship AI products in days: Use AI to generate your backend, iterate in your IDE, deploy to production infrastructure instantly

Build backend templates: Package entire backend architectures as code and share via GitHub

Collaborate across skill levels: Developers write code, product owners validate visually, everyone stays aligned

Version control everything: Your backend logic lives in git alongside your frontend code

Scale without rewrites: Start fast, grow to production scale without architectural changes

Try It Yourself

Xano 2.0 is available now. If you're building and tired of choosing between speed and production-readiness, this is worth exploring.

Try Xano 2.0 today here!
Check out the Launch Event here

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What I'm curious about:

• If you're building something, what's your biggest bottleneck right now? Is it speed of iteration? Infrastructure complexity? Team collaboration? Let me know in the comments.

• If you're learning to build, what's your biggest challenge? If you're using AI, do you feel like you're learning to code and understanding how everything ties together?

PS Apologies in advance for the long post and poor formatting! AI couldn't help me polish this post, but it can help you ship your backend today! lol

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