TL;DR

• Supabase wins for AI startups needing Postgres, pgvector for embeddings, and SQL flexibility -best developer experience for complex data models.
• Firebase excels at rapid prototyping and mobile-first apps with real-time sync, but NoSQL limitations hurt AI use cases requiring joins and vector search.
• AWS Amplify best for teams already deep in AWS ecosystem or needing fine-grained infrastructure control -steeper learning curve, higher operational overhead.

Jump to Decision framework · Jump to Database comparison · Jump to Vector storage for AI · Jump to Auth and security · Jump to Real-time capabilities · Jump to Cost analysis · Jump to Migration considerations

Supabase vs Firebase vs AWS Amplify for AI Startups

Choosing a backend-as-a-service (BaaS) platform shapes your startup's technical foundation for years. For AI startups, the decision hinges on vector storage (for embeddings), database flexibility (for complex queries), real-time capabilities (for live UI updates), and cost predictability. Here's how Supabase, Firebase, and AWS Amplify compare -and which to choose based on your use case.

Key takeaways

• Supabase: Best for AI startups needing Postgres + pgvector; strongest SQL capabilities; $25–500/month typical cost.
• Firebase: Fastest to prototype; excellent mobile SDK; NoSQL limits complex AI queries; $50–800/month typical.
• AWS Amplify: Most flexible; tightest AWS integration; highest complexity; $100–1,200/month typical.

Decision framework

Choose based on your startup's priorities and constraints.

Choose Supabase if:

• You're building AI features requiring vector search (semantic search, RAG, recommendations).
• Your data model needs complex queries (joins, aggregations, full-text search).
• You prefer SQL over NoSQL.
• Developer experience and speed matter (best docs, fastest setup).
• Budget-conscious (generous free tier; predictable pricing).

Best for: AI-powered SaaS, RAG applications, B2B tools, complex data models.

Choose Firebase if:

• You're building mobile-first apps (iOS/Android SDKs are best-in-class).
• You need real-time sync for collaborative features (chat, multiplayer, live dashboards).
• Your data model is simple (no complex joins or aggregations).
• You value Google ecosystem integration (Auth, Analytics, Cloud Functions).
• Rapid prototyping speed trumps scalability concerns.

Best for: Mobile apps, real-time collaboration tools, MVPs, consumer apps.

Choose AWS Amplify if:

• You're already using AWS services (SageMaker, Bedrock, Lambda, S3).
• You need enterprise compliance (HIPAA, SOC 2, custom VPCs).
• You require fine-grained infrastructure control (custom networking, security policies).
• You have DevOps expertise on the team.

Best for: Enterprise AI products, regulated industries, AWS-native architectures.

Database comparison

Database architecture fundamentally shapes what you can build.

Supabase: Postgres (relational SQL)

Database: PostgreSQL 15+ with pgvector extension.

Strengths:

• Mature SQL: Full ACID transactions, foreign keys, complex joins, aggregations.
• pgvector: Native vector storage and similarity search for embeddings (cosine, L2, inner product).
• Full-text search: Built-in tsvector for semantic + keyword hybrid search.
• JSON support: JSONB columns for flexible schema alongside relational structure.
• Extensions: PostGIS (geospatial), pg_cron (scheduled jobs), timescaledb (time-series).

Limitations:

• Horizontal scaling requires manual sharding (vs Firebase's auto-scaling).
• Real-time subscriptions less mature than Firebase (improving rapidly).

Best for: AI apps needing vector search, complex queries, relational data integrity.

Example use case: RAG application storing document embeddings (pgvector), user permissions (relational), and conversation history (JSONB).

Firebase: Firestore (NoSQL document store)

Database: Firestore (NoSQL) or Realtime Database (JSON tree).

Strengths:

• Real-time sync: Changes propagate to clients instantly; excellent for collaborative apps.
• Auto-scaling: Google handles scaling; no DB administration.
• Offline support: Mobile SDKs cache data locally; sync when online.
• Simple queries: Fast document lookups, collection queries.

Limitations:

• No joins: Must denormalise data (duplicate across collections).
• No vector search: No native embeddings support; requires external service (Pinecone, Weaviate).
• Query limitations: No OR queries, limited aggregations, no full-text search.
• Cost surprises: Reads/writes scale linearly with usage; expensive at high volume.

Best for: Mobile apps, real-time collaboration, simple data models.

Example use case: Chat app with message sync, user presence, typing indicators -real-time is killer feature.

AWS Amplify: DynamoDB or RDS (your choice)

Database: DynamoDB (NoSQL) or RDS Postgres/MySQL (relational) -you pick.

Strengths:

• Flexibility: Choose DB based on use case (DynamoDB for key-value, RDS for relational).
• AWS integration: Native connections to Lambda, S3, SageMaker, Bedrock.
• Fine-grained control: VPC networking, IAM policies, custom backup strategies.

Limitations:

• Complexity: More configuration than Supabase/Firebase; steeper learning curve.
• Operational overhead: You manage scaling, backups, monitoring (unless using managed RDS).
• DynamoDB quirks: Single-table design patterns require expertise; no joins.

Best for: AWS-native architectures, enterprise compliance needs, teams with DevOps resources.

Example use case: AI platform using SageMaker for model inference, S3 for data storage, Lambda for orchestration -Amplify ties it together.

Feature comparison table

Verdict for AI startups: Supabase wins on database flexibility for AI use cases requiring embeddings + relational queries.

For database architecture patterns, see /blog/database-postgres-vs-mongodb-startups.

Vector storage for AI

Vector embeddings power semantic search, RAG, recommendations -critical for AI products.

Supabase: pgvector (native Postgres extension)

How it works:

• Install pgvector extension (one-click in Supabase dashboard).
• Store embeddings as vector(1536) column (e.g., OpenAI text-embedding-3-small).
• Query with similarity functions: <=> (cosine), <-> (L2), <#> (inner product).

Example query (semantic search):

SELECT content, embedding <=> '[0.1, 0.2, ...]'::vector AS similarity
FROM documents
ORDER BY similarity
LIMIT 10;

Performance: HNSW indexing for fast approximate nearest neighbor (ANN) search; sub-100ms on 1M vectors.

Cost: Included in Supabase pricing; no separate vector DB fees.

Best for: RAG applications, semantic search, recommendation engines.

Firebase: No native support

Workaround: Use external vector DB (Pinecone, Weaviate, Qdrant) + Firebase for app data.

Architecture:

1. Store document metadata in Firestore (title, URL, user_id).
2. Store embeddings in Pinecone (with Firestore doc_id as reference).
3. Query Pinecone for nearest vectors; fetch metadata from Firestore.

Drawback: Added complexity, latency, cost ($70+/month for Pinecone).

Best for: Teams already using Firebase who can tolerate external dependency.

AWS Amplify: RDS Postgres + pgvector (manual setup)

How it works:

• Provision RDS Postgres instance via Amplify.
• Manually install pgvector extension.
• Use same pgvector queries as Supabase.

Drawback: More setup than Supabase (no one-click); higher cost (RDS instance fees).

Alternative: Use Amazon OpenSearch Service for vector search (more expensive, complex).

Best for: AWS-native teams who need vector search but want full infrastructure control.

Auth and security

All three platforms provide authentication; differences lie in flexibility and customisation.

Supabase Auth

Features:

• Email/password, magic links, OAuth (Google, GitHub, Apple, etc.).
• Row-level security (RLS) policies in Postgres (SQL-based access control).
• JWT tokens; works with any frontend framework.
• Anonymous auth (for waitlists, guest access).

Strengths:

• RLS is powerful: WHERE user_id = auth.uid() enforces multi-tenancy at DB level.
• Flexible OAuth providers (20+ supported).

Limitations:

• RLS policies can get complex; requires SQL expertise.

Best for: B2B SaaS with complex permission models (orgs, roles, teams).

Firebase Auth

Features:

• Email/password, phone, OAuth (Google, Facebook, Apple, etc.).
• Security rules (declarative access control in Firestore/Storage).
• Deep mobile SDK integration (biometric auth, SMS).

Strengths:

• Best mobile auth experience (native iOS/Android SDKs).
• Security rules simpler than SQL RLS (for basic use cases).

Limitations:

• Security rules less expressive than SQL (can't join collections).
• Phone auth expensive (SMS costs).

Best for: Mobile apps, consumer apps with simple permissions.

AWS Amplify Auth (Cognito)

Features:

• Email/password, OAuth, SAML, custom auth flows.
• User pools (manage users), identity pools (federated identities).
• MFA, advanced security (risk-based auth, account takeover protection).

Strengths:

• Enterprise-grade (HIPAA, SOC 2).
• Fine-grained IAM integration (tie auth to AWS resource permissions).

Limitations:

• Complex setup; steep learning curve.
• User experience (UI) less polished than Supabase/Firebase.

Best for: Enterprise apps, regulated industries, AWS-native stacks.

Security comparison table

Verdict: Firebase wins on mobile auth UX; Supabase wins on flexibility (RLS); Amplify wins on enterprise compliance.

For auth best practices, see /blog/ai-onboarding-process-startups.

Real-time capabilities

Real-time updates power collaborative features, live dashboards, chat apps.

Supabase Realtime

How it works: PostgreSQL LISTEN/NOTIFY broadcasts DB changes to subscribed clients via WebSockets.

Features:

• Subscribe to table changes (inserts, updates, deletes).
• Row-level filtering (e.g., "only rows where org_id = 123").
• Broadcast (send messages to all connected clients).
• Presence (track who's online).

Strengths:

• SQL-native (no separate real-time DB).
• RLS policies apply to real-time subscriptions (secure by default).

Limitations:

• Less mature than Firebase (fewer features, occasional latency spikes).
• Not ideal for high-frequency updates (e.g., multiplayer games).

Best for: Live dashboards, collaborative editing, real-time notifications.

Firebase Realtime Database / Firestore

How it works: Native real-time sync; changes propagate to all subscribed clients instantly.

Features:

• Real-time listeners on documents/collections.
• Offline persistence (mobile SDKs cache locally).
• Conflict resolution (last-write-wins or custom).

Strengths:

• Best-in-class real-time (Google's core competency).
• Offline-first mobile experience.

Limitations:

• Real-time tied to Firestore; can't use with external DB.

Best for: Chat apps, multiplayer games, collaborative tools (Google Docs-style).

AWS Amplify + AppSync

How it works: AWS AppSync (managed GraphQL) with subscriptions over WebSockets.

Features:

• GraphQL subscriptions for real-time data.
• Integrates with DynamoDB, Lambda, RDS.

Strengths:

• GraphQL flexibility (query exactly what you need).
• Tight AWS integration.

Limitations:

• Complex setup (GraphQL schema, resolvers, IAM policies).
• Higher latency than Firebase.

Best for: AWS-native apps needing GraphQL + real-time.

Real-time comparison table

Verdict: Firebase wins on real-time maturity; Supabase strong for SQL-native real-time; Amplify viable for GraphQL-first architectures.

For real-time architecture patterns, see /blog/product-operations-playbook-ai.

Cost analysis

Pricing models differ dramatically; costs scale non-linearly with usage.

Supabase pricing

Free tier:

• 500MB database
• 1GB file storage
• 2GB bandwidth
• 50,000 monthly active users

Pro tier ($25/month):

• 8GB database (+ $0.125/GB beyond)
• 100GB file storage (+ $0.021/GB)
• 250GB bandwidth (+ $0.09/GB)
• Daily backups

Typical startup cost:

• Pre-launch / MVP: $0 (free tier)
• 1K users, 10K requests/day: $25–50/month
• 10K users, 100K requests/day: $100–200/month
• 100K users, 1M requests/day: $500–1,000/month

Cost drivers: Database size, bandwidth, compute (for complex queries).

Firebase pricing

Free tier (Spark):

• 1GB Firestore storage
• 10GB bandwidth
• 50K reads/day, 20K writes/day

Pay-as-you-go (Blaze):

• $0.18 per 100K document reads
• $0.54 per 100K document writes
• $0.02 per GB stored
• $0.12 per GB bandwidth

Typical startup cost:

• Pre-launch / MVP: $0 (free tier)
• 1K users, 50K reads/day: $30–80/month
• 10K users, 500K reads/day: $200–500/month
• 100K users, 5M reads/day: $1,000–2,500/month

Cost drivers: Read/write volume (scales linearly); can explode with poorly optimised queries.

Warning: Real-time listeners count as continuous reads; costs add up fast.

AWS Amplify pricing

Pricing: Complex (depends on services used: DynamoDB, RDS, Lambda, S3, Cognito, AppSync).

Typical startup cost (DynamoDB + Lambda + Cognito + AppSync):

• Pre-launch / MVP: $50–100/month (minimum viable setup)
• 1K users, 10K requests/day: $150–300/month
• 10K users, 100K requests/day: $500–800/month
• 100K users, 1M requests/day: $1,200–2,500/month

Cost drivers: Lambda invocations, DynamoDB read/write units, AppSync queries, data transfer.

Advantage: AWS Free Tier (12 months) offsets early costs.

Cost comparison table (10K users, 100K requests/day)

Verdict: Supabase most cost-effective for AI startups; Firebase mid-range (watch read/write costs); Amplify most expensive (but justified if AWS-native).

For cost optimisation strategies, see /blog/startup-pricing-strategy-b2b-saas.

Migration considerations

Switching platforms mid-flight is painful -choose wisely upfront.

Migrating from Firebase to Supabase

Why migrate:

• Hit Firebase cost ceiling (reads/writes too expensive).
• Need SQL queries (joins, aggregations).
• Want vector search (pgvector).

Migration complexity: High (NoSQL → SQL requires data model redesign).

Steps:

1. Design relational schema (normalise Firestore collections).
2. Export Firestore data (JSON).
3. Transform and import into Postgres (custom scripts).
4. Rewrite queries (Firestore SDK → SQL).
5. Migrate auth users (Supabase supports Firebase imports).

Estimated time: 2–6 weeks for mid-sized app.

Migrating from Supabase to AWS Amplify

Why migrate:

• Need enterprise compliance (HIPAA, custom VPC).
• Want tighter AWS service integration (SageMaker, Bedrock).

Migration complexity: Medium (Postgres → RDS Postgres is straightforward).

Steps:

1. Provision RDS Postgres via Amplify.
2. Dump Supabase database (pg_dump).
3. Restore to RDS (pg_restore).
4. Rewrite Supabase client calls to use RDS connection.
5. Migrate auth to Cognito (export users, reimport).

Estimated time: 1–3 weeks.

Migrating from AWS Amplify to Supabase

Why migrate:

• Reduce complexity (Amplify too heavyweight).
• Lower costs.
• Better developer experience.

Migration complexity: Medium (if using RDS Postgres); High (if using DynamoDB).

Steps:

1. Export data from RDS/DynamoDB.
2. Import into Supabase Postgres.
3. Rewrite Amplify SDK calls to Supabase client.
4. Migrate Cognito users to Supabase Auth.

Estimated time: 1–4 weeks.

General rule: Migrating SQL → SQL is easier than NoSQL → SQL or vice versa.

Call-to-action (Decision stage) Map your requirements (vector storage? mobile-first? AWS-native?) to the decision framework -pick the platform that fits your constraints.

FAQs

Can you use Supabase with AWS services (Lambda, S3)?

Yes. Supabase is just Postgres + APIs; integrate with AWS via standard Postgres drivers or Supabase REST API. Not as tight as Amplify, but viable.

Is Firebase dead? Should startups avoid it?

No. Firebase is alive and widely used. Avoid if you need SQL, vector search, or complex queries. Great for mobile + real-time MVPs.

Can you self-host Supabase to save costs?

Yes. Supabase is open-source; self-hosting saves money but adds operational overhead. Recommended only if you have DevOps resources.

Which platform has the best TypeScript support?

All three have excellent TypeScript SDKs. Supabase auto-generates types from your database schema (best DX).

Summary and next steps

For AI startups, Supabase offers the best balance of vector storage (pgvector), SQL flexibility, cost efficiency, and developer experience. Choose Firebase if mobile-first + real-time is critical and data model is simple. Choose AWS Amplify if you're AWS-native or need enterprise compliance.

Next steps

1. Map your requirements: vector storage? complex queries? mobile-first? AWS-native?
2. Prototype with your top choice (all have generous free tiers).
3. Build sample RAG pipeline to test vector search performance.
4. Evaluate costs at 10K users, 100K requests/day using pricing calculators.

Internal links

• /blog/database-postgres-vs-mongodb-startups
• /blog/ai-onboarding-process-startups
• /blog/product-operations-playbook-ai
• /blog/startup-pricing-strategy-b2b-saas
• /blog/vercel-vs-netlify-vs-railway-deployment

External references

• Supabase Documentation – comprehensive guides and API reference.
• Firebase Documentation – official Firebase guides.
• AWS Amplify Documentation – AWS Amplify setup and tutorials.
• pgvector GitHub – Postgres vector extension for embeddings.
• Postgres vs MongoDB for Startups (Thoughtbot, 2024) – database architecture patterns.

Crosslinks

• See also /blog/typescript-vs-python-startup-stack
• /blog/rest-api-design-best-practices