Postgres extension complements pgvector for performance and scale

Summary

pgvectorscale pgvectorscale builds on pgvector with higher performance embedding search and cost-efficient storage for AI applications. pgvectorscale complements pgvector, the open-source vector data extension for PostgreSQL, and introduces the following key innovations for pgvector data: A new index type called StreamingDiskANN, inspired by the DiskANN algorithm, based on research from Microsoft. Statistical Binary Quantization: developed by Timescale researchers, This compression method improves on standard Binary Quantization. Label-based filtered vector search: based on Microsoft's Filtered DiskANN research, this allows you to combine vector similarity search with label filtering for more precise and efficient results. On a benchmark dataset of 50 million Cohere embeddings with 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. 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. Application developers or DBAs can use pgvectorscale with their PostgreSQL databases. If you want to contribute to this extension, see how to build pgvectorscale from source in a developer environment and our testing guide. For production vector workloads, get private beta access to vector-optimized databases with pgvector and pgvectorscale on Timescale. Sign up here for priority access. Installation The fastest ways to run PostgreSQL with pgvectorscale are: Using a pre-built Docker container Run the TimescaleDB Docker image. Connect to your database: psql -d...