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.

DiskANN is a graph-based approximate nearest neighbor search (ANNS) system optimized for fast and accurate billion-point nearest neighbor search on a single node, leveraging SSD storage. It is highly relevant for large-scale vector database applications requiring efficient vector search at scale.

cuVS is an open-source library from RAPIDS for fast, GPU-accelerated vector search, useful for building high-performance vector databases.

Amazon OpenSearch's k-NN plugin enables scalable, efficient vector search using ANN algorithms (IVF, HNSW) directly within a managed OpenSearch cluster. It is directly relevant for building, querying, and scaling vector databases on AWS.

An open-source library for approximate nearest neighbor search in high-dimensional spaces, often used as a backend for vector databases and search engines.

HNSWLIB is a C++ library with Python bindings for fast approximate nearest neighbor search using Hierarchical Navigable Small World (HNSW) graphs, commonly used in vector database backends.