Avoid setting up new database infrastructure by adding vector search directly to PostgreSQL for free. But lags on 10M+ vector scales.
pgvector works well when you need to query vectors alongside relational data within an existing PostgreSQL database. The friction starts when running memory-intensive index builds on large datasets, as Postgres lacks a native way to throttle these resource-heavy operations. Before buying, compare vs Milvus, a purpose-built vector database designed to handle highly distributed vector workloads without manual sharding.
Oleh KemFounder & Lead Analystpgvector stores embeddings as a native column type and queries them with standard SQL, avoiding the operational complexity of a separate vector database for applications already running on Postgres.
pgvector's HNSW index achieves sub-50ms similarity search for collections under 10M vectors, covering most product recommendation and semantic search use cases without a specialized vector database.
pgvector writes and deletes embeddings within standard Postgres transactions, ensuring vector index and application data never diverge in multi-step operations that require rollback.
Prices last verified June 28, 2026
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Consistently rated vector databases software built for Teams on PostgreSQL wanting built-in vector search who need focused workflows.
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