Qdrant
High-performance vector search
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Data Infrastructure High-performance vector search
Performing high-performance vector similarity search with advanced filtering, payload storage, and quantization for memory efficiency. Qdrant excels at production workloads that need fast filtering on metadata alongside vector search, such as multi-tenant RAG systems.
Run Qdrant locally with Docker (docker pull qdrant/qdrant && docker run -p 6333:6333 qdrant/qdrant) or use the managed cloud at cloud.qdrant.io. Install the Python client with pip install qdrant-client. Create a collection with your vector dimension and start upserting points with vectors and payloads.
$ pip install qdrant-client Be the first to share a Qdrant case study and get discovered by clients.
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