Supabase vs Firebase vs Convex for AI Apps (2026)

Supabase vs Firebase vs Convex for AI Apps (2026)

For most AI apps in 2026, Supabase is the default pick because it is Postgres with pgvector, so retrieval, permissions and business data share one SQL database. Convex is the better choice for realtime, collaborative TypeScript apps. Firebase fits mobile-first products on Google Cloud and is the only one with managed Middle East regions.

All three are backend-as-a-service platforms that save you from building auth, storage, realtime and APIs by hand, which is exactly why founders reach for them when building an MVP. But AI apps stress a backend differently from a classic CRUD app. You need embeddings and vector search, long-running model calls, streaming, per-tenant data isolation, and good observability. Those needs expose real differences between the three.

We build on all three at NomadX, an AI-native software studio in Dubai, depending on the product. This comparison is based on each platform’s official documentation as of October 2026, plus what we see when AI coding agents work in each codebase.

What is the quick comparison?

Here is the side-by-side. Details and caveats for each row follow below.

Prices are list prices from each vendor’s pricing page in October 2026 and change often, so treat them as a guide to the pricing model rather than a quote.

How do the data models differ?

Supabase gives you a full Postgres database. Tables, joins, foreign keys, transactions and SQL. Access control uses Postgres row-level security, so a policy like “users see rows in their own workspace” is enforced by the database itself, whichever client queries it.

Firebase centres on Firestore, a document database of collections and documents. It is fast to start with and excellent for app-shaped data, but relational questions (“all invoices for customers in this region with overdue tasks”) get awkward, and you end up denormalising data. In 2026, Firebase renamed Data Connect to Firebase SQL Connect, which puts a GraphQL-style layer over Cloud SQL for PostgreSQL. That gives Firebase a real relational option, but it is a separate product with its own region list.

Convex stores documents in tables with an optional schema, and you write every query and mutation as a TypeScript function that runs inside the database with transactional guarantees. It feels like writing backend code rather than SQL, and relationships are handled with IDs and indexes.

What it means for AI apps: most AI SaaS products end up relational. Documents belong to workspaces, workspaces have members, members have roles, usage maps to billing. That shape fits Postgres naturally, which is a big reason Supabase is our default in the best tech stack for an AI SaaS MVP.

Which has the best vector search for RAG?

Supabase wins for most RAG workloads because pgvector lives in the same database as your permissions. One SQL query can filter by tenant, check access, run a vector similarity search and join back to document metadata. You can also combine it with Postgres full-text search for hybrid retrieval, which often beats pure vector search on real business documents.

Firebase added native Firestore vector search: you store embeddings as vector fields, create a vector index, and query with nearest-neighbour search, optionally pre-filtered on other fields. Per the Firestore vector search docs, embeddings can have up to 2,048 dimensions and the index type is flat. Firestore does not create the embeddings, so you call an embedding model yourself. It works well for moderate collections and Firebase-native apps.

Convex has built-in vector indexes with 2 to 4,096 dimensions and equality filters on up to 16 fields. One important design constraint from the Convex vector search docs: vector search runs only inside actions, not queries or mutations, and returns IDs and scores that you then load in a separate query. That is fine for RAG, since you call an LLM from an action anyway, but it means retrieval is not transactional with the rest of the read. Convex also bills each vector search by the size of the index searched.

For more on retrieval design (chunking, reranking and agentic retrieval), see our agentic RAG guide.

Which is best for realtime AI features?

Convex is the strongest here. Every query is reactive by default: when the underlying data changes, every client subscribed to that query updates automatically. For AI features like a shared document with an AI assistant, live agent progress, or collaborative chat, you write normal queries and the realtime behaviour comes for free.

Firebase has had excellent realtime listeners for years, plus an offline cache on mobile that the others do not match out of the box. For a mobile app that must work on a flaky connection, this is a real advantage.

Supabase Realtime offers three tools: listening to Postgres changes, Broadcast for low-latency messages, and Presence for who-is-online state. It works well, but you wire subscriptions explicitly rather than getting reactivity on every query.

For streaming LLM tokens to the UI, all three are usually paired with an HTTP streaming endpoint (for example with the Vercel AI SDK) rather than pushing each token through the database.

How do auth and server logic compare?

All three handle standard auth (email, magic links, OAuth providers). The difference is how auth connects to data access.

Supabase Auth feeds directly into row-level security policies, which is the cleanest model for multi-tenant B2B apps. Server logic lives in Edge Functions (Deno) or Postgres functions.

Firebase Auth is the most mature for mobile, including phone number sign-in, and connects to Firestore security rules. For AI features, Firebase offers Firebase AI Logic for calling Gemini models from apps and Genkit as a server-side framework for AI flows.

Convex offers Convex Auth and integrates with third-party providers such as Clerk and WorkOS. Auth checks happen inside your TypeScript functions, which is explicit and easy to test. Actions can call any LLM provider, and scheduled functions handle long-running jobs.

If you are building for UAE government or regulated users, any of the three can sit behind UAE Pass via OIDC; our UAE Pass integration guide explains the flow.

What about pricing models?

The three charge in different units, and the unit matters more than the headline price.

• Supabase: a Free plan (2 active projects, paused after a week of inactivity) and a Pro plan from $25/month that includes $10 of compute credit, then usage for compute, disk, egress and monthly active users beyond the included amounts. Predictable for most SaaS workloads. See Supabase pricing.
• Firebase: the free Spark plan and the pay-as-you-go Blaze plan. Firestore bills per document read, write and delete, plus storage. A UI that re-reads large collections often can surprise you, so design queries and listeners carefully.
• Convex: Free & Starter with pay-as-you-go beyond the included limits, and Professional at $25 per developer per month with larger included function calls, storage and bandwidth. EU deployments cost 30% more on paid plans’ resource pricing, according to Convex’s EU launch notes.

For an AI app, the database bill is usually small next to LLM spend. Put your energy into model routing and caching first; our guide on cutting LLM costs shows where the money goes.

Can you self-host, and which regions are available in the Middle East?

This is where the three differ most for Gulf companies, and where many comparisons are out of date.

Supabase is open source and self-hostable with Docker Compose. Self-hosted Supabase lacks some managed features, including branching, managed backups and point-in-time recovery, and the Management API, and you take on security, upgrades and high availability yourself. The managed service runs in 17 AWS regions as of October 2026, with no Middle East region; the closest are Mumbai and Frankfurt. See the Supabase regions list.

Firebase cannot be self-hosted (the emulators are for local development only). But Firestore supports Middle East locations: Doha (me-central1), Dammam (me-central2) and Tel Aviv (me-west1). That is useful for Gulf latency, but it is not UAE-located data. Firebase SQL Connect has its own, shorter region list, so check it separately.

Convex Cloud runs in US East (N. Virginia) and, since February 2026, EU West (Ireland), on every plan. The backend is self-hostable via Docker or a binary, on SQLite or Postgres, under the FSL Apache 2.0 licence. A deployment’s region cannot be changed later without exporting and reimporting data.

What this means for UAE data residency: none of the three offers a managed UAE region today. If your sector, customers or contracts require personal data to stay in the UAE, the practical paths are self-hosted Supabase or self-hosted Convex in AWS me-central-1 or Azure UAE North, or plain managed Postgres with pgvector in those regions. Our PDPL compliance guide covers what residency actually requires.

Which works best with AI coding agents?

Convex and Supabase are the most agent-friendly in our experience, for different reasons.

With Convex, schema, queries, mutations and actions are all TypeScript in your repository. An agent like Claude Code sees the whole backend contract, generated types flow to the frontend, and the type checker catches a large share of agent mistakes before tests run.

With Supabase, agents benefit from decades of public SQL and Postgres knowledge. Keep migrations in the repo, generate TypeScript types from the schema, and agents can reason about the data model reliably. Row-level security policies need careful human review, because a wrong policy fails silently.

Firebase works too, but security rules and composite indexes are easy to get subtly wrong, and those mistakes often show up only in production. Add rules tests in CI from day one.

All three offer MCP servers so agents can inspect schemas, data and logs directly. Point them at development projects, keep production access read-only, and review every migration. This fits the spec-first, test-on-every-change process we describe in spec-driven development.

Which should you choose?

Pick by the shape of your product, not by popularity.

You can also mix: Supabase for core data with a Convex or Firebase layer for a realtime feature is possible, but in an MVP one backend is almost always the better call.

How does this fit a 7-day MVP?

A backend-as-a-service is one of the reasons a focused product can reach production in a week. Auth, storage, realtime and an API exist from hour one, so the build days go to the product. In our 7-day MVP cadence, the backend choice is made on Day 1: Spec & architecture, the Day 2-3 clickable prototype runs on a real development project with seed data, Day 4-6 covers build and test with automated tests on every change, and Day 7 is production launch with monitoring and handover.

If you want help picking or building on one of these, see Backend & API Engineering, LLM App Development, our TypeScript and Next.js stack page, and the full software development hub. For the end-to-end process, read how we ship an MVP in 7 days.

The bottom line

Supabase is the default for AI SaaS because Postgres plus pgvector keeps everything in one place. Convex is the best developer experience for realtime TypeScript apps. Firebase remains the strongest mobile platform and the only one with managed Middle East regions. And if your data must stay in the UAE, plan to self-host or use managed Postgres in a UAE cloud region.

Not sure which fits your product? Talk to us and we will give you a straight recommendation.