Convex vs Supabase: Quick Verdict
Convex vs Supabase is one of the more interesting backend comparisons in 2026 because the two products have the same surface area — real-time data, authentication, serverless functions, storage — but fundamentally different philosophies underneath.
Choose Convex if you want a backend where real-time reactivity is the default, your data model is document-oriented, and you're building a single-page app or AI product where live state synchronization matters everywhere.
Choose Supabase if your team knows Postgres, you need SQL query power, you're migrating an existing relational schema, or you need the ecosystemic breadth that comes with a battle-tested relational database.
Both are excellent. The choice mostly comes down to data model preference and how much real-time you need by default.
Architecture: The Core Difference
This is the most important thing to understand before picking.
Supabase is Postgres plus a managed layer. It adds real-time via Postgres logical replication (listening to DB changes and broadcasting over WebSockets). Every query is a SQL query. The data model is relational. All Postgres extensions, RLS (Row Level Security), and ecosystem tools work because it's just Postgres underneath.
Convex is a purpose-built reactive database. It's not Postgres. Queries are written as TypeScript functions. When any data a query depends on changes, all subscribed clients automatically receive updated results — without you writing any subscription logic. This reactivity is end-to-end and automatic, not bolted on.
The implication: in Supabase, you add real-time selectively, on specific tables, with explicit subscription setup. In Convex, everything is reactive by default, and you opt out if you don't need live updates.
Data Model
| | Convex | Supabase | |-|--------|----------| | Storage type | Document (JSON-like) | Relational (Postgres) | | Query language | TypeScript functions | SQL + PostgREST + TypeScript | | Schema | Optional TypeScript schema | SQL migrations | | Relations | Explicit references by ID | Foreign keys + joins | | Full-text search | Built-in | Postgres full-text or pgvector | | Vector search | Not native | pgvector extension | | Migrations | Push-based, schema sync | SQL migration files |
For AI applications, Supabase's pgvector integration is a meaningful advantage. If you're building RAG pipelines or semantic search, Postgres with pgvector is a well-understood, production-proven stack. You don't need a separate vector database.
Convex doesn't have native vector support as of 2026. You'd need an external vector store (Pinecone, Weaviate) alongside it.
Real-Time and Reactivity
This is where Convex genuinely excels.
In Convex, you write a query function (TypeScript). Any component that uses that query automatically re-renders when the underlying data changes — with no extra code. The subscription is implicit. You write the query once; Convex handles invalidation and push updates automatically.
// Convex query — automatically reactive everywhere it's used
export const getMessages = query(async ({ db }) => {
return await db.query("messages").order("desc").take(50);
});
In Supabase, real-time requires explicit subscription setup:
// Supabase real-time — explicit subscription setup required
const channel = supabase
.channel('messages')
.on('postgres_changes', { event: '*', schema: 'public', table: 'messages' },
payload => handleChange(payload))
.subscribe();
For apps where live state synchronization is the core feature — collaborative tools, live feeds, multiplayer products, real-time dashboards — Convex's model significantly reduces the code you write and debug. For apps where most queries are one-shot (page load, form submit), Supabase's selective real-time is more than enough.
Serverless Functions
Both platforms offer serverless functions for backend logic.
Convex functions come in three flavors: queries (read-only, cached), mutations (transactional writes), and actions (arbitrary async operations, including third-party API calls). The transactional guarantee on mutations is strong — writes are atomic, and the database state is always consistent. This matters for AI products where agent actions need to write state reliably.
Supabase Edge Functions are Deno-based serverless functions deployed globally. They're less opinionated — write any code, call any API. The tradeoff is that you're responsible for your own transaction logic when combining multiple Postgres writes.
For AI products, Convex's actions are useful for running LLM calls and writing results atomically — the action pattern maps well to agent workflows. Supabase Edge Functions work well for webhook handlers, background jobs, and API proxies.
Authentication
Both include authentication:
- Supabase Auth — Full-featured, supports email/password, magic link, OAuth (Google, GitHub, etc.), phone, and SAML for enterprise. Row Level Security integrates deeply with auth user IDs.
- Convex Auth — Added more recently, supports email/password and OAuth. Less mature than Supabase Auth but improving rapidly.
For auth-heavy applications, Supabase is more battle-tested. Both work fine for standard social login flows.
Pricing
| Usage | Convex | Supabase | |-------|--------|----------| | Free tier | 1M function calls/month | 500MB DB, 2GB bandwidth | | Entry paid | ~$25/month | $25/month (Pro) | | DB size limits | Document count-based | Postgres storage | | Function execution | Billed per call + compute | Billed per invocation | | Real-time connections | Included | Included (Pro) |
At small scale, both are effectively free. At production scale, cost comparisons depend heavily on your read/write patterns. Convex's billing model (per function call) can get expensive for read-heavy applications with many live subscribers. Supabase's model (Postgres storage + bandwidth) is more predictable for traditional CRUD apps.
Developer Experience
Both have excellent documentation and active communities.
Convex DX highlights:
- TypeScript end-to-end, no schema drift between frontend and backend
- Hot reload in development — mutations and queries update instantly
- Built-in dashboard for inspecting live DB state and function logs
- Automatic caching and invalidation — you don't configure this
Supabase DX highlights:
- Familiar SQL and Postgres tooling for teams with database experience
- Dashboard is excellent — table editor, SQL editor, realtime inspector
- Large ecosystem of community libraries and extensions
- Local development via Supabase CLI with Docker
Teams coming from a SQL background or migrating from Firebase/Postgres will feel comfortable in Supabase immediately. Teams starting fresh who want the tightest possible TypeScript integration may prefer Convex.
Which Is Better for AI Products?
For AI applications specifically, consider:
- Need vector search? Supabase + pgvector is the easier path.
- Need real-time agent state? Convex's reactive model handles live agent status updates elegantly.
- Need complex analytics queries? Supabase — SQL handles this; Convex doesn't.
- Need strong transactional writes for agent actions? Convex mutations provide strong consistency guarantees.
- Need file/object storage? Both offer storage; Supabase's is more mature.
We generally recommend Supabase as the default for AI products because of pgvector and the maturity of the Postgres ecosystem. See our comparison of Firebase vs Supabase for AI apps for a related angle. Convex is the better choice when live state synchronization is the core product value.
Related: PostgreSQL vs MongoDB for AI · Firebase vs Supabase for AI Apps · What is a Vector Database?
[Building an AI product and unsure which backend fits your architecture? Book a 15-min scope call → — we'll give you a concrete recommendation based on your data model and scale requirements.]
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