Open-source vector database with hybrid search built in
Weaviate is an open-source vector database that stores objects and their embeddings, then supports semantic, keyword and hybrid search over them. It can run in Docker on your own machine, in Kubernetes, or as Weaviate Cloud, so the same APIs work whether you self-host or buy the managed service. Modules handle vectorisation for you, calling embedding providers directly so you can insert raw text instead of computing vectors yourself. The differentiator is hybrid retrieval: combining keyword scoring with vector similarity often beats either method alone on real queries.
Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.
hybrid search, rag retrieval, self-hosted vector search, multi-tenant knowledge bases
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Pure vector search misses exact terms like product codes and names, while keyword search misses paraphrases. Hybrid search runs both and merges the scores, which usually improves recall on real queries.
Yes. Weaviate is open source and can be self-hosted with Docker or Kubernetes at no licence cost, so you only pay for the machines it runs on. The managed cloud has a free sandbox tier.
Not always. Vectoriser modules call embedding providers for you, so you insert raw text and Weaviate handles vectorisation. You can also supply your own vectors if you already generate them.
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