Yes. Built in Rust with sub-millisecond query times for production workloads.

Does Qdrant support filtering?

Yes. Combine vector similarity with payload filtering for precise results.

Can I self-host Qdrant?

Yes. Docker deployment with horizontal scaling support.

Does Qdrant support Python?

Yes. Python, JavaScript, Go, and Rust client libraries.

Does Qdrant handle large datasets?

Yes. Disk-based indexing handles datasets larger than available RAM.

Does Qdrant support clustering?

Yes. Horizontal scaling with sharding and replication.

How does Qdrant compare to Weaviate?

Weaviate has built-in vectorizers. Qdrant is faster with better filtering. Both are excellent open-source vector databases.

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Qdrant — Vector Database Built for Production AI

Qdrant is an open-source vector similarity search engine built in Rust for production workloads. Store millions of embeddings, query by similarity with filtering, and serve real-time AI applications with consistently low latency.

Production-Grade Performance

Built in Rust for speed and reliability. Qdrant handles millions of vectors with sub-millisecond query times. Horizontal scaling distributes data across nodes. Disk-based indexing handles datasets larger than RAM.

Filtered Vector Search

Most vector databases search by similarity alone. Qdrant combines vector similarity with payload filtering — find the most similar vectors WHERE category equals "electronics" AND price is under $50. This is essential for real-world AI applications.

Qdrant vs Pinecone

Pinecone is fully managed with simpler setup. Qdrant is open-source with self-hosting and more advanced filtering. Pinecone for quickest setup. Qdrant for control and cost savings.