Supabase

Open-source Firebase alternative built on Postgres with a first-class pgvector AI toolkit.

Freemium· Free / Pro $25 per month / Team $599 per month / Enterprise custom. Compute add-ons from $10/mo (Micro) to $3,730/mo (16XL). Database overage $0.125/GB. PITR $100/mo per 7-day retention.RAG

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Best for

Full-stack and AI developers building RAG apps, semantic search, or agent memory who want embeddings, auth, storage, and business data in one Postgres database.

Skip if

Teams that need a specialist billion-scale vector database, or non-technical users who want a no-code chatbot builder without writing SQL or backend code.

Supabase is an open-source backend-as-a-service built on Postgres that has become one of the most popular vector stores for AI applications. It packages a dedicated Postgres database, auto-generated REST and GraphQL APIs (via PostgREST and pg_graphql), row-level-security auth with 20+ social providers, S3-compatible object storage with a 285+ city CDN, realtime subscriptions, and serverless Edge Functions behind a single dashboard and CLI. For AI teams the headline feature is the deep pgvector integration: you can store embeddings alongside your transactional data, index them with IVFFlat or HNSW, and query them with SQL joins instead of maintaining a separate vector database. The platform ships an AI toolkit with LangChain, LlamaIndex, and OpenAI examples, plus a hosted vector-enabled storage bucket type. Typical workflows include RAG chatbots that keep documents, chunks, embeddings, and chat history in one schema; semantic search over product catalogs; hybrid keyword-plus-vector retrieval; and agent memory backed by Postgres. Developers get a generous free tier (2 projects, 500 MB, unlimited API requests), a straightforward $25/month Pro plan, a self-hostable Docker stack for teams that need to keep data on their own infrastructure, and enterprise options with SOC2/ISO 27001 compliance and BYO-Cloud deployments. It suits full-stack developers who want a Postgres-first alternative to Firebase without adopting a proprietary vector service, and teams migrating off single-purpose vector DBs who want to consolidate on SQL.

Editor's take

Supabase is our default recommendation when a project needs both a real backend and a vector store. Keeping embeddings inside Postgres eliminates a whole class of sync bugs, and the open-source stack means you are never locked in. It is not the fastest vector DB at extreme scale, but for the 95% of RAG apps under a few million chunks, one Postgres is the right answer.

— The AI Tool Bible editorial team

Pros

✅ pgvector is deeply integrated so embeddings live in the same Postgres schema as your business data, enabling SQL joins between rows and vectors.
✅ Fully open source and self-hostable via Docker, so you can move off the hosted platform without rewriting your app.
✅ Auto-generated REST and GraphQL APIs plus row-level security remove huge amounts of backend boilerplate for AI app prototypes.
✅ Realtime subscriptions and Edge Functions let you stream RAG results and run inference glue code close to the database.
✅ Generous free tier with unlimited API requests and 2 projects makes it easy to prototype AI features without a credit card.
✅ First-party guides and starters for LangChain, LlamaIndex, OpenAI, and Hugging Face reduce the time to a working RAG stack.

Cons

⚠️ It is a Postgres backend platform, not an AI product per se, so you still need to bring your own embedding model, LLM, and orchestration layer.
⚠️ pgvector at very high dimensionality or billion-scale corpora can lag purpose-built vector databases like Pinecone or Milvus on latency and index build time.
⚠️ Free-tier projects pause after a week of inactivity, which surprises hobbyists running demo RAG bots.
⚠️ The jump from $25 Pro to $599 Team is steep for small teams that need SOC2 or SLAs.
⚠️ Self-hosting the full stack (Studio, GoTrue, Storage, Realtime, Kong, PostgREST) is doable but operationally heavier than a managed vector service.

Use cases

RAG chatbot backendsemantic document searchhybrid keyword and vector searchagent long-term memoryembedding storage for product catalogsAI-powered mobile app backenduser authentication for LLM appsfile storage for RAG source documentsrealtime AI chat interfacesself-hosted vector database