pgvector vs Pinecone

pgvector 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.

Pinecone's HNSW-based index returns approximate nearest neighbor results for 1B+ vector collections at under 100ms p99 latency, serving production semantic search without managing index infrastructure.

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.

Pinecone's hybrid search runs dense embedding search and sparse keyword search simultaneously, improving recall for domain-specific queries where pure semantic search misses exact-match technical terms.