JaguarDB is a database solution, identified as a vector database in the context of the provided research.

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.

EFANNA is an extremely fast approximate nearest neighbor search algorithm based on kNN graphs and randomized KD-trees. The provided implementation offers a high-performance ANN index suitable as a building block in custom vector search and retrieval infrastructure.

PilotANN is a memory-bounded GPU-accelerated framework for large-scale vector search, designed to improve performance and efficiency of approximate nearest neighbor (ANN) search workloads, making it relevant as a high-performance engine/component in vector database and vector search systems.

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.

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.