Qdrant
Open-source vector search engine written in Rust, with filtering that works properly alongside similarity.
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Link checked 19 September 2026: this site responded and still names the product.
About Qdrant
What it is
Qdrant is an open-source vector database. It stores embeddings with arbitrary metadata attached and searches by similarity with filters applied during the search rather than after it, and runs self-hosted, as a managed cloud, in your own account, or at the edge.
Why it's different
Filtering during search is the detail that matters in practice. Most engines either filter before, which wrecks the index, or after, which means asking for ten results and getting two once the filter applies. Qdrant's approach is the reason it holds up on real queries with real constraints. It is open source with a clean API and modest resource use; the limits are that it does vectors and not text ranking, so hybrid search means combining it with something else.
How people use it
Retrieval-augmented generation with per-user or per-tenant filtering. Semantic search inside an existing application. Deduplication and recommendation. Run it locally first - it is a single container - because the fastest way to learn what your embeddings actually retrieve is to query them yourself.
Written by the n3os team. We are not affiliated with Qdrant.
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