jvector is a high-performance Java-based library and engine for vector search and approximate nearest neighbor indexing.

NVIDIA CAGRA is a GPU-accelerated graph-based library for approximate nearest neighbor searches, optimized for high-performance vector search leveraging modern GPU parallelism. It is suitable for scenarios requiring rapid, large-scale vector retrieval.

BANG is a billion-scale approximate nearest neighbor search system optimized for single GPU execution, enabling high-performance vector search in vector database environments at massive scale.

NGT (Neighborhood Graph and Tree) is an open-source vector search engine designed for fast and scalable approximate nearest neighbor search.

KDB is a high-performance vector database supporting billion-scale vector search, with features aimed at enterprises needing large-scale vector storage and retrieval.

Billion-scale graph-based ANNS index with direct insertion capabilities. Achieves <1ms search latency with >10x less memory than in-memory indexes through GC-free design and update combining.