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pgvectorscale complements pgvector, the open-source vector data extension for PostgreSQL, and introduces the following key innovations for pgvector data:
On benchmark dataset of 50 million Cohere embeddings (768 dimensions each), PostgreSQL with pgvector and pgvectorscale achieves 28x lower p95 latency and 16x higher query throughput compared to Pinecone's storage optimized (s1) index for approximate nearest neighbor queries at 99% recall, all at 75% less cost when self-hosted on AWS EC2.
PostgreSQL with pgvector and pgvectorscale extensions outperformed Pinecone’s storage optimized (s1) and performance-optimized (p2) pod-based index types.
To learn more about the performance impact of pgvectorscale, and details about benchmark methodology and results, see the pgvector vs Pinecone comparison blog post.
In contrast to pgvector, which is written in C, pgvectorscale is developed in Rust using the PGRX framework, offering the PostgreSQL community a new avenue for contributing to vector support.
