AI engineers building RAG
Use Qdrant to store document embeddings with metadata filters for retrieval-augmented generation.
Skip if:
Skip if your app can use a small in-process vector index with no service to operate.
Open source alternative to Pinecone, Weaviate Cloud and Zilliz Cloud
Last verified
Search embeddings with a self-hosted vector database for RAG, semantic search, and recommendations, backed by Rust performance and Apache-2.0 licensing.
Use Qdrant to store document embeddings with metadata filters for retrieval-augmented generation.
Skip if your app can use a small in-process vector index with no service to operate.
Use Qdrant to add similarity search over products, documents, images, or support content.
Skip if exact keyword search in PostgreSQL or Elasticsearch fully solves the problem.
AI applications need to store embeddings and search nearest neighbors quickly, but managed vector databases can become expensive as collections and query volume grow. Teams also need metadata filters, data ownership, and predictable deployment choices.
Qdrant stores vectors with payload metadata and exposes APIs for search, filtering, and point management.
The README highlights extended filtering support, useful for semantic search, faceted search, recommendation, and neural matching workflows.
Qdrant provides REST and client libraries for Go, Rust, JavaScript, Python, .NET, and Java, plus a Docker quick-start command.
To self-host Qdrant locally, start the server with Docker so your app can reach the REST and gRPC APIs on port 6333.
```bash
docker pull qdrant/qdrant
docker run -p 6333:6333 qdrant/qdrant
```Qdrant is an open source vector database and vector similarity search engine.
Yes. The README shows a local Docker command and links to installation and security docs.
Qdrant can replace Pinecone or managed vector search services when self-hosting and data control matter.
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Run Qdrant on your own server
Hostinger sets Qdrant up as a one-click app on a VPS.
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