Postgres has long been the workhorse of relational databases. Now it is becoming an AI-first database too. With native vector indexes (HNSW and IVFFlat) integrated into Postgres core extensions, developers no longer need separate vector databases to power semantic search, recommendation systems, or AI retrieval workflows.
References:
Why This Matters
- One database, fewer moving parts: store relational and vector data together
- Performance: HNSW indexes speed up nearest-neighbor search
- Ecosystem: seamless use with ORMs, SQL clients, and cloud Postgres providers
What Changed
- IVFFlat was already supported but required careful tuning
- HNSW is now available and generally faster for approximate nearest neighbor (ANN) queries
- Parallel search and better
LIMITquery optimizations reduce latency
Example Setup
Click "Show Code" to view the code snippet
Querying Vectors
Click "Show Code" to view the code snippet
Best Practices
- Dimension must match the embedding model (e.g., 768 for OpenAI
text-embedding-3-small) - Normalize vectors before insert if you plan cosine similarity
- Use batch inserts for efficiency and VACUUM ANALYZE after large imports
Real World Use Cases
- Semantic document search directly inside Postgres
- Hybrid queries combining metadata filters with ANN
- AI copilots and RAG pipelines without a separate vector DB
Final Thought
Native vector indexing makes Postgres a true all-in-one database for modern apps. Instead of bolting a separate vector database onto your stack, you can now build AI search features directly in the same Postgres instance powering your business logic. For many teams, that means faster iteration, simpler infra, and fewer 3am pager calls.