Fully managed serverless vector database optimized for high-QPS semantic search in AI apps. Features pod/serverless indexing, hybrid sparse-dense, metadata filtering, auto-scaling. Use cases: LLM RAG pipelines, real-time personalization. Comparisons: Easier than Milvus for cloud-only, but no self-host; vs Qdrant: more serverless focus.

A commutative vector quantization method for KV cache compression that reduces FP16 cache size by 87.5% with 2-bit quantization and enables 1-bit quantization, allowing LLaMA-3.1 8B to run with 128K context on a single RTX 4090 GPU.

High-performance vector search library with product quantization.

ClickHouse column type for query-time vector precision tuning in hybrid analytical SQL+vector searches. Enables flexible recall/speed tradeoff with ACID-like features on columnar relational infra.

nanopq is a lightweight product quantization library for efficient vector compression and similarity search, which is an important feature for vector databases that need to store and query large-scale vector data efficiently.

Scalable Nearest Neighbors library by Google Research that provides efficient vector similarity search at scale. Uses anisotropic vector quantization and advanced compression techniques to handle twice as many queries per second compared to alternatives.