How Lovable’s Supabase Integration Changed the Game

Four months ago, we shipped a game-changing native integration with Supabase.

Let’s rewind, take a deep dive, and understand why MCP (Model Context Protocol) may be the next big deal.

Why We Needed Supabase

Lovable started as a front-end-only text-to-app platform. It was great for building websites but limited when it came to production apps. To build real applications, you almost always need:

A database to store data
Authentication to manage users
Code execution to handle business logic

Supabase was the obvious choice for our backend needs. Here’s why:

Open source—self-hosting means no vendor lock-in
Built on Postgres—the only valid database choice
Works with all major auth providers
Generous free tier—great for early-stage projects
More than a database—realtime, storage, background tasks, and more
User-friendly UI—crucial for non-technical users

But if Supabase already has a well-documented SDK, why did we need a deep integration?

One Word: Context

In the world of LLMs, context is everything. The key to making AI useful is providing it with the right information at the right time.

Too little context → The AI misses the point
Too much context → The AI gets confused (or runs out of memory)

So, we asked ourselves: What’s the best context we can give an LLM to understand what needs to be done?

To understand a backend, you need:

Database schema—tables, relationships, and structure
Secrets & API keys—so users don’t need to input them manually
Logs & errors—to debug issues automatically

The Supabase API exposed (almost) everything we needed to do this!

The Hard Part: Edge Functions

One big problem: Supabase edge functions weren’t deployable via the API. You needed the Supabase CLI, which meant Docker.

This was a deal-breaker for our users.

So we got creative. We found a way to run the Supabase CLI behind the scenes, giving us full control over backend deployment while keeping the process seamless.

This turned out to be a huge advantage. Lovable wasn’t just integrating with Supabase—it was orchestrating Supabase.

We built:

A feedback loop where Lovable could read & modify the entire backend
The smoothest Supabase developer experience for AI-driven apps

And the results?

Lovable grew by over $1M in ARR per week, scaling from under $500K to $20M ARR.

Enter MCP: The Next Evolution

But this level of deep integration took a ton of work.

To make Supabase “LLM-friendly,” we had to build a translation layer on top of their API—concise, structured, goal-oriented.

What if Supabase (or any SaaS provider) did that work instead? That’s the promise of MCP (Model Context Protocol). MCP is a standardized way for SaaS platforms to expose resources, tools, and prompts optimized for LLMs.

Instead of Lovable doing the work to structure Supabase data for AI, Supabase itself would provide an MCP server designed for AI interactions.

This flips the incentives:

SaaS companies now have to build the best MCP server to make AI work seamlessly with their product.
AI platforms (like Lovable) can just connect instead of building custom integrations.

What This Means for the Future

In a post-MCP world, building a Supabase integration could be as simple as connecting to their MCP server.

Faster integrations—we can add way more backends, much more quickly.
Smarter agents—LLMs get structured, high-quality context without extra work.
More powerful AI apps—seamless automation, without the hacky workarounds.

Should we do it? 😏