Question: Can you recommend an open-source solution for high-dimensional vector search with fast query performance?

If you're looking for an open-source option for high-dimensional vector search with high query performance, Milvus is a great option. It offers scalable vector database management with a range of options to fit your needs, from Milvus Lite for prototyping to Milvus Distributed for large-scale enterprise performance. The database is fast for search, thanks to its Global Index, and has a range of tools and community-driven development.

Another powerful option is Zilliz, which is based on Milvus and offers a managed vector database service tuned for massive scale vector data. It offers 10x faster vector retrieval speed and 99.95% monthly uptime, making it a good option for high-availability applications. Zilliz supports a range of use cases, including recommender systems and multimodal similarity search, and is available in multiple clouds and offers a pay-as-you-go pricing model.

Pinecone is another good option, with a serverless design for fast querying and retrieval of similar matches across billions of items. It supports low-latency vector search and hybrid search with keyword boosting, and is enterprise-ready with SOC 2 and HIPAA certifications. Pinecone offers flexible pricing options and integrates with major cloud providers, so it's a good option for scaling and security.

Last, Qdrant is a cloud-native vector database designed for fast and scalable vector similarity searches. Written in Rust, it's designed to get the best performance out of high-dimensional vectors and offers strong security. Qdrant supports use cases like advanced search and recommendation systems, and offers flexible deployment options including a free tier, so it's a good option for different deployment scenarios.