Comparison

Busabase vs Supabase for AI Agents

Supabase's own guidance is the clearest statement of the problem this page is about: "We strongly discourage connecting the Supabase MCP server to your production database." That is correct advice. It also leaves a question open — if your agent should not write to production, where does the work it produces actually go?

This is not a comparison of two products competing for the same job. Supabase is an application backend and a very good one. Busabase is a workspace and system of record agents write into. The useful question is which layer your problem lives on, and the honest answer for a large number of teams is "both."

Last verified: 17 September 2026, against Supabase's published agent documentation and engineering blog.

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The short verdict

Choose Supabase when

you are building an application. Auth, storage, edge functions, OLTP, realtime, vector recall — this is what it is for, and Busabase is not competing for it.

Choose Busabase when

the agent's own output is the deliverable: records, documents, research, and files that people and later agent runs read as fact, where a confidently wrong write is expensive.

Use both when

and this is the common case — your agent builds an app that runs on Supabase, while the agent's working material and the decisions about it live somewhere a person can review.

What Supabase says about agents

Supabase positions itself as "the complete Postgres developer platform built for agentic workloads," and the pitch is to stop "stitching together separate services for memory, vectors, auth, file storage, and APIs." It ships an MCP server so an agent can query data, run migrations, and deploy functions; Agent Skills to give agents procedural knowledge; and Row Level Security so "every database call respects tenant boundaries."

That is a coherent, well-executed answer to the question where does my application's data live.

The part Supabase is unusually honest about

Their own engineering post — "AI Agents Know About Supabase. They Don't Always Use It Right." — catalogues what goes wrong when agents work against their platform. Their list, not ours:

agents "skip RLS policies on exposed schemas";

they create views without security_invoker = true, "which silently bypasses RLS";

they miss that "UPDATE requires a SELECT policy. Without one, updates silently return 0 rows";

they "hallucinate CLI commands that don't exist";

they "ignore the docs entirely, relying on training data that may be months out of date";

and, plainly: "agents are lazy about it."

Their conclusion follows from the evidence: keep the agent on a local or staging database, not production.

We are not quoting this to score a point. It is the most accurate public description of agent behaviour against a real backend that either company has published, and the recommendation that follows from it is the right one.

Why this is a layering fact, not a Supabase flaw

Notice what every item on that list has in common. They are failures of correctness, and the mechanism being asked to catch them — RLS — governs permission.

RLS answers: may this caller write this row? It is very good at that. It cannot answer: is this the right value? An agent with entirely correct permissions, writing a confidently wrong number into a column it is fully authorized to write, passes every check the database has. Nothing in the engine is wrong. The record is just false.

This is why "keep agents off production" is sound advice rather than a workaround — at the engine layer there is no other answer available. The check that would catch a wrong-but-permitted write does not live in the database. It lives in the write path.

So where does the agent's work go?

If production is off limits, teams usually end up with one of three arrangements, and the first two are worse than they look:

The agent writes to staging and a human copies things across.

Correct, and it throws away most of the speed the agent was for.

The agent writes to files or a chat log and someone reconciles later.

Fast, and the work stops existing as data — the next agent run cannot read it, and nothing can be attributed.

The agent writes to a store that expects agent writes

where a material write can arrive as a proposal with a diff, be inspected, and only then become canonical.

The third is what Busabase is. Not a safer database engine — the same Postgres underneath — but a different contract at the moment of writing.

Comparison by the job you are trying to do

What you are trying to do	Supabase	Busabase
Give an application a backend	Exactly what it is for	Not its job
Auth, storage, edge functions, realtime	Yes	No
OLTP and high-frequency machine writes	Yes	No — use Supabase
Vector recall / RAG	Yes	No — use Supabase or a vector store
Control which caller may write	RLS, mature and granular	Permissions per node and per operation
Control whether a written value becomes canonical	Not at this layer	Change Request with field-level diff
Let an agent write to production safely	Explicitly discouraged	The designed case
Keep source, proposer, reviewer, and history on a record	Application's responsibility	Built in
Give people a UI to review agent output	Build it yourself	The product

Where Supabase is the better choice

You are shipping software.

If the artifact is an application with users, Supabase is the answer and this comparison is not close.

You need database primitives.

Migrations, connection pooling, edge functions, realtime subscriptions, mature Postgres tooling.

The writes are machine-scale.

Telemetry, events, high-frequency updates. A review path is meaningless at that volume, and we would not pretend otherwise.

Your agents are coding agents.

If the agent's job is to write code and run migrations, Supabase's MCP server and Agent Skills are aimed directly at that, and their Skills work is genuinely good.

Where you need something else

The agent's output is the product: enriched records, research, documents, structured knowledge that people rely on.

A wrong value is expensive and not obviously wrong — the case RLS structurally cannot catch.

A person needs to see what would change before it changes, without you building that review UI yourself.

You need provenance on the record — who proposed, who approved, from what source — not just who held the connection string.

Using them together

The split most teams land on is straightforward:

Supabase runs the app your agent builds. Busabase is where the agent itself works.

An agent researches and enriches records in Busabase; a person reviews the material writes; approved records are read out through Busabase's API by the application, which stores its own operational data in Supabase. Neither product is doing the other's job, and neither one has to be replaced for the other to be useful.

Honest limitations

Busabase is not a database platform. There is no connection pooler, no edge functions, no realtime subscriptions, no migration tooling for your app's schema, and no vector index. If you came looking for those, Supabase is not just the better option — it is the only option of the two.

Busabase also adds a step that is genuinely unnecessary for a large amount of agent work. Low-consequence writes should be fast, and we keep them fast, but if nothing your agents write matters enough to inspect, the review path is overhead you do not need.

Frequently asked questions

Does Busabase replace Supabase?

No. Different layers. If your application needs a backend, it still needs one.

Is Busabase just Postgres with an approval queue?

It runs on Postgres, but the product is the workspace: Bases, documents, files, Skills, and small apps that agents and people share, with review applied to writes that matter.

Why not solve this with RLS and better prompts?

RLS cannot evaluate whether a value is true, and prompts cannot make an agent reliable — Supabase's own post is a catalogue of agents ignoring instructions that were available to them.

Can my agent write to Busabase and Supabase in the same run?

Yes, and that is a normal pattern: operational data to the application backend, material and reviewable output to the workspace.

What about Butterbase and other agent-backend products?

The same layering applies — they answer "where does the application's data live," not "where does the agent's work become trustworthy." We have done first-hand research on Supabase and are scoping this page to Supabase rather than asserting things about products we have not examined as closely.

Next step

If the write-path argument is the part you want to test, the fastest way is to connect an agent you already use and watch one material write arrive as something you can inspect.

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Sources: Supabase for Agents, "AI Agents Know About Supabase. They Don't Always Use It Right.", Supabase AI Tools documentation. Quotations are Supabase's own words as published on the verification date above.