JaguarDB

seekdb is OceanBase’s experimental vector database component for high-performance nearest neighbor search over embedding vectors.

AI data lake with revolutionary index-on-the-lake technology enabling sub-second queries from S3. Features 10x cost efficiency vs in-memory DBs and 2x faster than alternatives. This is a commercial platform with OSS components.

Qdrant is an open‑source vector database designed for high‑performance similarity search and AI applications such as RAG, recommendation systems, advanced semantic search, anomaly detection, and AI agents. It provides scalable storage and retrieval of vector embeddings with features like filtering, hybrid search, and production‑grade APIs for integrating with machine learning workloads.

Built into the Salesforce platform, Data Cloud Vector Database ingests various large datasets from customer interactions, classifies and organizes unstructured data, and merges it with structured data to enrich customer profiles and store as metadata in Data Cloud. It enhances generative AI by providing more relevant, accurate, and up-to-date responses through improved data retrieval and semantic search capabilities.

SIGMOD 2025 paper introducing Tribase, a vector data query engine that uses triangle inequalities for reliable and lossless pruning compression, achieving efficient similarity search without sacrificing accuracy.

A GPU-accelerated vector query processing system that supports large vector datasets beyond GPU memory. RUMMY uses reordered pipelining to efficiently overlap data transmission and GPU computation, achieving up to 135× better performance than traditional GPU-based approaches.