Supabase vs Firebase vs Convex: Backend for AI Applications

AI applications require backends that handle vector embeddings, real-time updates for streaming responses, and flexible data models for evolving AI outputs. Supabase, Firebase, and Convex each approach these requirements differently. We built AI features on all three to compare.

Quick verdict

Our recommendation: Use Supabase for most AI applications - native pgvector support and familiar SQL make RAG pipelines straightforward. Choose Convex for real-time AI features requiring instant updates. Use Firebase when mobile and Google Cloud integration matter more than AI-specific features.

AI backend requirements

AI applications have distinct needs beyond traditional CRUD:

We evaluated each platform against these requirements.

Supabase

Overview

Supabase provides a Postgres database with authentication, storage, and real-time capabilities. The pgvector extension enables native vector operations.

Vector capabilities

import { createClient } from '@supabase/supabase-js';

const supabase = createClient(url, key);

// Store embedding
const { error } = await supabase
  .from('documents')
  .insert({
    content: documentText,
    embedding: embedding, // 1536-dim vector
    metadata: { source: 'upload' }
  });

// Vector similarity search
const { data } = await supabase.rpc('match_documents', {
  query_embedding: queryEmbedding,
  match_threshold: 0.8,
  match_count: 10
});

The RPC function:

create function match_documents(
  query_embedding vector(1536),
  match_threshold float,
  match_count int
)
returns table (id uuid, content text, similarity float)
language sql stable
as $$
  select id, content, 1 - (embedding <=> query_embedding) as similarity
  from documents
  where 1 - (embedding <=> query_embedding) > match_threshold
  order by embedding <=> query_embedding
  limit match_count;
$$;

Rating: 5/5 for vector capabilities. Full pgvector power including HNSW indexes.

Real-time features

Supabase Realtime broadcasts database changes:

// Subscribe to AI job updates
const channel = supabase
  .channel('job-updates')
  .on(
    'postgres_changes',
    {
      event: 'UPDATE',
      schema: 'public',
      table: 'ai_jobs',
      filter: `id=eq.${jobId}`
    },
    (payload) => {
      updateJobStatus(payload.new);
    }
  )
  .subscribe();

Rating: 3/5 for real-time. Works but adds 50-200ms latency. Not ideal for streaming AI responses.

Strengths

Full Postgres: All SQL capabilities including joins, transactions, and advanced queries.

pgvector native: No external vector database needed. One platform for all data.

Open source: Self-host option for data sovereignty requirements.

RLS security: Row-level security for multi-tenant AI applications.

Weaknesses

Real-time latency: Database-change-driven updates are slower than purpose-built real-time.

Scaling complexity: High-volume vector search may require dedicated instances.

Edge functions limited: 50s timeout constrains long AI operations.

Pricing

Vector operations don't incur additional costs beyond database compute.

Firebase

Overview

Firebase provides a document database (Firestore), authentication, hosting, and cloud functions. It's deeply integrated with Google Cloud.

Vector capabilities

Firebase doesn't have native vector support. Options:

Option 1: Vertex AI Vector Search

import { VertexAI } from '@google-cloud/vertexai';

// Separate vector index on Vertex AI
const vertexai = new VertexAI({ project, location });
const index = vertexai.getIndex('documents-index');

const results = await index.findNearest({
  vector: queryEmbedding,
  topK: 10
});

// Join with Firestore data
const docs = await Promise.all(
  results.map(r => firebase.firestore().doc(`documents/${r.id}`).get())
);

Option 2: Extension (community)

// Firebase extensions for Pinecone/Weaviate exist but add complexity

Rating: 2/5 for vectors. Requires external services and additional complexity.

Real-time features

Firestore real-time is excellent:

import { onSnapshot } from 'firebase/firestore';

// Real-time AI job status
const unsubscribe = onSnapshot(
  doc(db, 'ai_jobs', jobId),
  (snapshot) => {
    const job = snapshot.data();
    updateUI(job.status, job.output);
  }
);

// Stream AI responses chunk by chunk
async function streamAIResponse(jobId: string, chunks: AsyncIterable<string>) {
  const jobRef = doc(db, 'ai_jobs', jobId);

  for await (const chunk of chunks) {
    await updateDoc(jobRef, {
      output: arrayUnion(chunk),
      updatedAt: serverTimestamp()
    });
  }
}

Rating: 5/5 for real-time. Sub-50ms updates, excellent client SDKs.

Strengths

Real-time excellence: Best-in-class real-time sync with offline support.

Mobile SDKs: Native iOS, Android, Flutter support with caching.

Google integration: Seamless with Cloud Functions, Vertex AI, Cloud Run.

Scaling handled: Auto-scales without configuration.

Weaknesses

No native vectors: Requires external vector database or Vertex AI.

NoSQL constraints: Complex queries and joins are awkward or impossible.

Pricing unpredictability: Document reads/writes can surprise you at scale.

Vendor lock-in: Deep Google integration makes migration difficult.

Pricing

AI applications with high read volumes (RAG lookups) can get expensive quickly.

Convex

Overview

Convex is a newer backend platform with real-time as a core primitive. TypeScript-first with automatic reactivity.

Vector capabilities

Convex added vector search support:

// convex/schema.ts
import { defineSchema, defineTable } from 'convex/server';
import { v } from 'convex/values';

export default defineSchema({
  documents: defineTable({
    content: v.string(),
    embedding: v.array(v.float64()),
    metadata: v.object({ source: v.string() })
  }).vectorIndex('by_embedding', {
    vectorField: 'embedding',
    dimensions: 1536
  })
});

// convex/documents.ts
import { query } from './_generated/server';
import { v } from 'convex/values';

export const search = query({
  args: { embedding: v.array(v.float64()), limit: v.number() },
  handler: async (ctx, { embedding, limit }) => {
    const results = await ctx.db
      .query('documents')
      .withIndex('by_embedding', (q) =>
        q.eq('embedding', embedding)
      )
      .take(limit);
    return results;
  }
});

Rating: 4/5 for vectors. Native support, slightly less mature than pgvector.

Real-time features

Real-time is Convex's core strength:

// Client-side with React
import { useQuery, useMutation } from 'convex/react';
import { api } from '../convex/_generated/api';

function AIChat() {
  // Automatically updates when job changes
  const job = useQuery(api.jobs.get, { jobId });
  const startJob = useMutation(api.jobs.start);

  return (
    <div>
      <button onClick={() => startJob({ prompt })}>Start</button>
      {job?.chunks.map(chunk => <span key={chunk}>{chunk}</span>)}
    </div>
  );
}

Streaming AI responses:

// convex/jobs.ts
import { mutation } from './_generated/server';

export const appendChunk = mutation({
  args: { jobId: v.id('jobs'), chunk: v.string() },
  handler: async (ctx, { jobId, chunk }) => {
    const job = await ctx.db.get(jobId);
    await ctx.db.patch(jobId, {
      chunks: [...job.chunks, chunk]
    });
  }
});

Rating: 5/5 for real-time. True reactive queries with sub-50ms updates.

Strengths

True reactivity: Queries automatically update when data changes. No polling or subscriptions to manage.

TypeScript end-to-end: Type-safe from database schema to client queries.

Transactions: ACID transactions simplify complex AI workflows.

Actions for external APIs: Long-running operations handled cleanly.

Weaknesses

Newer platform: Smaller community, fewer examples for AI patterns.

No raw SQL: Query language is powerful but different from SQL.

Pricing uncertainty: Usage-based model needs evaluation at scale.

Limited regions: Fewer deployment regions than Firebase/Supabase.

Pricing

Vector searches count as function calls.

Feature comparison

AI-specific patterns

RAG pipeline

Supabase approach:

// 1. Store document with embedding
await supabase.from('documents').insert({
  content, embedding, metadata
});

// 2. Query for relevant docs
const { data: relevantDocs } = await supabase.rpc('match_documents', {
  query_embedding: await embed(question),
  match_count: 5
});

// 3. Generate response
const response = await llm.complete(
  buildPrompt(question, relevantDocs)
);

Clean SQL-based RAG. Single platform for all data.

Convex approach:

// 1. Store with vector index
await ctx.db.insert('documents', { content, embedding, metadata });

// 2. Vector search
const relevantDocs = await ctx.db
  .query('documents')
  .withIndex('by_embedding', q => q.eq('embedding', queryEmbedding))
  .take(5);

// 3. Generate in action (for external API calls)
// convex/actions/generate.ts

TypeScript-native RAG with automatic types.

Streaming AI responses

Supabase approach:

// Use Realtime channel for updates
const channel = supabase.channel(`job-${jobId}`);

// Server pushes chunks
async function* generateAndStream(jobId: string, prompt: string) {
  for await (const chunk of llm.stream(prompt)) {
    await supabase.from('job_chunks').insert({ job_id: jobId, chunk });
    yield chunk;
  }
}

Works but not real-time's primary use case.

Convex approach:

// Client automatically sees updates
const job = useQuery(api.jobs.get, { jobId });
// job.chunks updates reactively as chunks are added

// Server action appends chunks
for await (const chunk of llm.stream(prompt)) {
  await ctx.runMutation(api.jobs.appendChunk, { jobId, chunk });
}

More natural reactive pattern for streaming.

Multi-tenant AI

Supabase approach:

-- RLS policy for multi-tenant isolation
create policy "Users see own org data"
on documents for select
using (org_id = auth.jwt() ->> 'org_id');

RLS handles isolation automatically.

Convex approach:

// Manual checks in queries
export const listDocuments = query({
  handler: async (ctx) => {
    const identity = await ctx.auth.getUserIdentity();
    return ctx.db
      .query('documents')
      .filter(q => q.eq(q.field('orgId'), identity.orgId))
      .collect();
  }
});

Explicit but type-safe filtering.

Use case recommendations

RAG-heavy application

Winner: Supabase

Native pgvector with HNSW indexes handles production RAG workloads. SQL flexibility for complex queries involving both vectors and metadata.

Real-time AI assistant

Winner: Convex

True reactive queries make streaming AI responses natural. TypeScript end-to-end reduces integration friction.

Mobile AI app

Winner: Firebase

Best mobile SDKs with offline support. Google Cloud integration for Vertex AI inference. Real-time sync for chat-style interfaces.

Multi-tenant SaaS

Winner: Supabase

Row-level security simplifies multi-tenant isolation. Postgres maturity for complex business logic alongside AI features.

Prototype/MVP

Winner: Convex

Fastest development experience. Automatic reactivity reduces boilerplate. Free tier sufficient for early validation.

Migration paths

Firebase to Supabase

Common for teams adding vector search:

// Export Firestore data
const snapshot = await admin.firestore().collection('documents').get();
const documents = snapshot.docs.map(doc => doc.data());

// Import to Supabase with embeddings
for (const doc of documents) {
  const embedding = await embed(doc.content);
  await supabase.from('documents').insert({
    ...doc,
    embedding
  });
}

Supabase to Convex

For better real-time performance:

// Supabase export
const { data } = await supabase.from('documents').select('*');

// Convex import
for (const doc of data) {
  await ctx.db.insert('documents', {
    content: doc.content,
    embedding: doc.embedding,
    metadata: doc.metadata
  });
}

Our verdict

Supabase is the most capable platform for AI applications today. Native pgvector support eliminates the need for external vector databases. Full Postgres gives you flexibility for complex queries, and RLS handles multi-tenancy elegantly. The real-time capabilities, while not best-in-class, are sufficient for most AI use cases.

Convex is the best choice when real-time performance matters most. The reactive query model is ideal for streaming AI interfaces. TypeScript-native development experience is excellent. Consider Convex for new projects where instant updates are critical.

Firebase remains strong for mobile-first applications and teams deep in the Google ecosystem. However, the lack of native vector support makes it less suitable for RAG-heavy applications. You'll need Vertex AI or an external vector database for serious AI features.

For most AI applications, start with Supabase. Move to Convex if real-time responsiveness becomes a bottleneck.

Further reading:

• /blog/supabase-vs-firebase-vs-planetscale-backend
• /blog/pinecone-vs-weaviate-vs-qdrant-vs-pgvector
• Supabase Documentation
• Firebase Documentation
• Convex Documentation