Convex vs Supabase for AI Apps

I have shipped apps with both Convex and Supabase. The Developers Digest site runs on Convex. Several DD ecosystem apps use Supabase. Here is an honest comparison for AI-powered applications.

Architecture Difference

Supabase is a Postgres database with auth, storage, and edge functions bolted on. You write SQL, use the PostgREST API, or use the JavaScript client. Your data is relational.

Convex is a reactive backend-as-a-service. You write TypeScript functions that run on Convex's infrastructure. Your data is document-based. Queries are reactive by default - when data changes, your UI updates automatically.

This is the core difference. Supabase gives you a database and lets you build everything else. Convex gives you a full backend runtime with the database included.

Real-Time for AI Features

AI apps need real-time updates. Streaming responses, live collaboration, status indicators.

Convex wins here. Every query is reactive. When data changes, connected clients update automatically. No WebSocket setup, no subscription management, no polling.

// Convex: reactive by default
const messages = useQuery(api.messages.list, { chatId });
// UI re-renders automatically when any message changes

Supabase has real-time via Postgres changes, but you manage subscriptions manually.

// Supabase: manual subscription
const channel = supabase
  .channel("messages")
  .on("postgres_changes", { event: "*", schema: "public", table: "messages" }, (payload) => {
    setMessages((prev) => [...prev, payload.new]);
  })
  .subscribe();

For a chat interface with streaming AI responses, Convex's automatic reactivity saves significant code.

Server Functions for AI

AI apps need server-side logic: calling APIs with secrets, processing results, chaining calls.

Convex actions are serverless functions that can call external APIs and are co-located with your schema.

// Convex action - calls AI API server-side
export const generateResponse = action({
  args: { prompt: v.string() },
  handler: async (ctx, { prompt }) => {
    const response = await anthropic.messages.create({
      model: "claude-sonnet-4-6",
      messages: [{ role: "user", content: prompt }],
    });
    await ctx.runMutation(api.messages.save, {
      content: response.content[0].text,
    });
  },
});

Supabase edge functions are Deno-based serverless functions deployed separately.

// Supabase edge function
Deno.serve(async (req) => {
  const { prompt } = await req.json();
  const response = await anthropic.messages.create({
    model: "claude-sonnet-4-6",
    messages: [{ role: "user", content: prompt }],
  });
  // Insert into database separately
  await supabase.from("messages").insert({ content: response.content[0].text });
  return new Response(JSON.stringify({ ok: true }));
});

Convex functions run in the same runtime as your database operations. Supabase edge functions are separate services that talk to your database over HTTP.

Cron Jobs for AI Workflows

Both support scheduled functions. AI apps commonly need them for: processing queues, periodic summaries, content generation.

Convex cron jobs are defined in TypeScript alongside your functions.

// convex/crons.ts
const crons = cronJobs();
crons.interval("process-queue", { minutes: 5 }, api.ai.processQueue);

Supabase uses pg_cron or external schedulers. More setup, but you get full SQL access.

Type Safety

Convex is fully typed. Schema defines types. Functions are typed. Client queries return typed data. End-to-end TypeScript with zero codegen friction.

Supabase generates types from your Postgres schema via CLI, but the chain can break. Schema changes require running supabase gen types again.

For AI apps that iterate fast, Convex's automatic type inference is a real productivity advantage.

Vector Search for RAG

If your AI app needs retrieval-augmented generation (RAG), you need vector search.

Supabase has pgvector built in. Full-featured vector search with indexing, filtering, and similarity functions. Mature and battle-tested.

Convex has vector search support but it is newer and less feature-rich than pgvector.

For RAG-heavy applications, Supabase's pgvector is the stronger choice today.

When to Use Each

Choose Convex when:

• Real-time UI is core (chat, collaboration, live dashboards)
• You want reactive queries without WebSocket management
• Your team is TypeScript-first
• You want server functions co-located with your schema
• You are building with Next.js and want the fastest integration

Choose Supabase when:

• You need relational data with complex queries
• Vector search (RAG) is a primary feature
• You want to use SQL directly
• You need row-level security for multi-tenant apps
• You want to self-host your backend

Choose both when:

• Convex for real-time features + Supabase for vector search/RAG
• This is a valid architecture that plays to each platform's strengths

Pricing

Both have generous free tiers for getting started.

Frequently Asked Questions

Can I use Convex and Supabase together?

Yes. Use Convex for real-time features and server functions, Supabase (with pgvector) for vector search and RAG. They complement each other well.

Which is better for a chat app with AI?

Convex. The reactive queries mean your chat UI updates automatically when new messages arrive. Streaming AI responses integrate naturally with Convex mutations.

Which has better TypeScript support?

Convex. Its type system is end-to-end - schema, functions, and client are all typed automatically. Supabase requires codegen and manual type maintenance.

Can I migrate from Supabase to Convex?

Yes, but the data model changes (relational to document). Your application logic needs rewriting since Convex functions replace edge functions and API routes.

Which scales better for AI workloads?

Both scale well. Supabase gives you more control over database optimization. Convex handles scaling automatically but you have less visibility into the infrastructure.