Compare Supabase, Firebase, and PlanetScale for AI application backends -evaluating vector search, real-time features, pricing, and which database best supports AI workloads.
TL;DR
| Feature | Supabase | Firebase | PlanetScale |
|---|---|---|---|
| Database | PostgreSQL | Firestore (NoSQL) | MySQL |
| Vector search | Yes (pgvector) | No | No |
| Real-time | Yes (PostgreSQL) | Yes (Firestore) | No |
| Auth | Built-in | Built-in | No |
| Storage | Yes (S3-compatible) | Yes (Cloud Storage) | No |
| Edge Functions | Yes (Deno) | Yes (Cloud Functions) | No |
| SQL access | Full PostgreSQL | No (NoSQL only) | Full MySQL |
| Self-hosted | Yes (open-source) | No | No |
Best for: AI applications with vector search, full-stack apps, developers who love PostgreSQL
Strengths:
Weaknesses:
AI-specific strengths:
Use cases:
Verdict: 4.7/5 - Best choice for AI applications requiring vector search.
Best for: Real-time mobile apps, rapid prototyping, Google ecosystem integration
Strengths:
Weaknesses:
AI-specific limitations:
Use cases:
Verdict: 4.2/5 - Excellent for mobile AI, but lack of vector search major limitation.
Best for: Massive-scale relational data, teams with MySQL expertise, non-AI workloads
Strengths:
Weaknesses:
AI-specific limitations:
Use cases:
Verdict: 3.8/5 - Excellent database, but not optimized for AI workloads. Choose only if MySQL required.
Task: Find 10 most similar documents to query embedding (1536 dimensions, 1M vectors)
SELECT id, content,
1 - (embedding <=> '[0.1, 0.2, ...]'::vector) AS similarity
FROM documents
ORDER BY embedding <=> '[0.1, 0.2, ...]'::vector
LIMIT 10;
Performance: 45ms (p95) with HNSW index Cost: Included in Supabase plan Complexity: Low (native SQL)
Not supported natively. Must use external vector DB:
// Store document in Firestore
await db.collection('documents').add({
content: "...",
embeddingId: "vec_123" // Reference to external vector DB
});
// Search via Pinecone/Qdrant
const results = await vectorDB.search(queryVector);
// Fetch metadata from Firestore
const docs = await Promise.all(
results.map(r => db.collection('documents').doc(r.id).get())
);
Performance: 80ms (p95, includes external API call) Cost: Firestore + vector DB subscription ($70+/month) Complexity: High (two systems to manage)
Not supported. Same external vector DB approach as Firebase.
Winner: Supabase by far -native vector search is game-changer for AI apps.
Scenario: Real-time chat with AI agent, 50 concurrent users
Supabase:
const channel = supabase
.channel('chat-room')
.on('postgres_changes',
{ event: 'INSERT', schema: 'public', table: 'messages' },
(payload) => console.log(payload)
)
.subscribe();
Latency: 100-200ms Concurrent connections: 10K+ (Pro plan) Cost: Included
Firebase:
db.collection('messages')
.orderBy('timestamp')
.onSnapshot((snapshot) => {
snapshot.docChanges().forEach(change => {
if (change.type === 'added') console.log(change.doc.data());
});
});
Latency: 50-100ms (faster than Supabase) Concurrent connections: 100K+ Cost: Included
PlanetScale: Not supported (polling required).
Winner: Firebase for real-time, but Supabase sufficient for most AI apps.
Scenario: AI chatbot with 100K messages/month, 5GB database, 10GB storage
Supabase:
Firebase:
PlanetScale:
Winner: Firebase cheapest for this scenario, but Supabase more predictable.
Time to first AI chatbot (with auth + database + real-time):
Supabase: 2-3 hours
# Setup
npx create-next-app my-app
npm install @supabase/supabase-js
# Enable pgvector, create tables, enable RLS, add Edge Function
# Total: ~2 hours for working AI chat with vector search
Firebase: 3-4 hours
# Setup
npx create-next-app my-app
npm install firebase
# Setup Firestore, Auth, Functions
# Add external vector DB (Pinecone)
# Total: ~3-4 hours (external DB adds complexity)
PlanetScale: 5-6 hours
# Setup
npx create-next-app my-app
npm install @planetscale/database
# Setup database, auth (external), storage (external), functions (external)
# Add vector DB (external)
# Total: ~5-6 hours (many external services)
Winner: Supabase for fastest AI app development.
Choose Supabase if:
Choose Firebase if:
Choose PlanetScale if:
Firebase → Supabase: Moderate (1-2 weeks)
Supabase → Firebase: Hard (2-4 weeks)
PlanetScale → Supabase: Easy (3-5 days)
Expert quote (Paul Copplestone, CEO of Supabase): "Firebase excels for real-time mobile apps, but if you're building AI with vector search, PostgreSQL + pgvector is the obvious choice. You get embeddings, relational data, and real-time in one database."
Athenic's production setup (50K+ users):
Why not Firebase: pgvector essential for RAG system. PostgreSQL handles complex multi-tenant queries better than NoSQL.
Why not PlanetScale: Needed vector search + real-time + auth in single platform. PlanetScale excellent database but not full backend.
Yes, common pattern. Use Pinecone/Qdrant for vectors, Firebase for everything else. Adds complexity and cost.
Yes, used by thousands of companies. Less battle-tested than Firebase but maturing rapidly.
All three excellent. Supabase auto-generates types from database schema (huge DX win).
Supabase: Yes (open-source). Firebase/PlanetScale: No.
Valid alternatives to Supabase if you only need database. Supabase adds auth/storage/functions/real-time for comprehensive backend.
Supabase best choice for AI applications due to native vector search (pgvector), combining embeddings, relational data, and real-time in single platform. Firebase best for mobile-first real-time apps willing to use external vector DB. PlanetScale excellent for massive-scale traditional workloads but not optimized for AI. For most AI builders, Supabase offers best balance of features, developer experience, and cost.
Winner: Supabase for AI applications.
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