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Managed vector database for AI applications
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Fast, scalable vector search engine for AI-driven applications
Qdrant delivers high-performance vector similarity search with payload filtering, hybrid sparse-dense queries, and horizontal scaling, enabling developers to build semantic search, recommendation, and classification systems quickly.

Qdrant is a Rust‑based vector similarity engine that stores vectors together with arbitrary JSON payloads. It lets developers perform nearest‑neighbor searches while filtering on metadata, making it suitable for semantic search, recommendation, and classification workloads.
The service supports dense vectors, sparse token‑level vectors for hybrid search, and built‑in vector quantization to cut RAM usage. Horizontal scaling is achieved through sharding and replication, with zero‑downtime rolling updates. Clients are available for Python, Go, Rust, JavaScript/TypeScript, .NET, and Java, and the API can be accessed via REST or gRPC. Qdrant can be run locally with Docker, embedded via the in‑memory Python client, or consumed as a fully managed Qdrant Cloud offering that includes a free tier.
Qdrant integrates with popular AI stacks such as LangChain, LlamaIndex, Haystack, and OpenAI’s retrieval plugins, allowing seamless use as a persistent vector store for large language model applications.
When teams consider Qdrant, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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Use the official Docker image (`docker run -p 6333:6333 qdrant/qdrant`) or the in-memory Python client (`QdrantClient(":memory:")`).
Official clients are available for Python, Go, Rust, JavaScript/TypeScript, .NET/C#, and Java.
Yes, it offers on-disk storage with vector quantization and async I/O, allowing data to survive restarts.
Qdrant Cloud provides a fully managed service with a free tier, handling scaling and maintenance.
Payloads are arbitrary JSON attached to vectors; queries can filter on any field using keyword, numeric range, geo-location, or full-text conditions combined with boolean clauses.
Project at a glance
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