All Tools
P
Vector DBFreeOpen Source
PGVECTORSCALE
Faster vector search for PostgreSQL at any scale
PostgreSQL
ABOUT
Standard pgvector indexes become slow and memory-intensive as the vector corpus grows beyond RAM capacity, making production-scale similarity search expensive and impractical. pgvectorscale brings DiskANN (Disk-based Approximate Nearest Neighbor) to PostgreSQL, enabling vector indexes that scale beyond memory limits by using SSD-optimized graph algorithms. This results in dramatically faster index builds, lower storage costs, and reliable query performance at any scale — all within the familiar PostgreSQL ecosystem.
INSTALL
docker pull timescale/timescaledb-ha:pg17
psql -d "postgres://<user>:<pass>@<host>:<port>/<db>" -c "CREATE EXTENSION IF NOT EXISTS vectorscale CASCADE;"
INTEGRATION GUIDE
1. Build production RAG pipelines with millions of embeddings stored and queried directly in PostgreSQL
2. Replace dedicated vector databases with PostgreSQL + pgvectorscale for simpler infrastructure
3. Run real-time semantic search on document corpora too large to fit in memory
4. Deploy cost-effective vector search at scale using commodity SSD storage instead of RAM-heavy solutions
5. Combine vector search with full PostgreSQL querying — joins, filters, aggregations, and transactions
TAGS
postgresqlvector-searchpgvectordiskannextensionrusttimescale