High-performance open-source vector database for production AI retrieval and RAG, for teams needing scale, hybrid search, or self-hosting.
What it does
Qdrant is a vector search engine built in Rust for storing and querying embeddings at scale. It supports hybrid dense/sparse search, advanced metadata filtering, and reranking, and can be deployed as open source, on Qdrant Cloud, in a hybrid setup, or at the edge.
How to use: Deploy Qdrant locally with Docker using the Quick Start Guide or the GitHub repository. Turn embeddings or neural network encoders into applications for matching, searching, and recommending.
Core features
Hybrid dense and sparse vector search (BM25, SPLADE, miniCOIL)
Advanced metadata filtering applied during search traversal
Multivector support for multimodal retrieval
Reranking with score boosting and late-interaction models (ColBERT, MMR)
Flexible deployment: cloud, hybrid, private, or edge
Rust-based engine optimized for low-latency, high-scale search
Best for
→Building retrieval-augmented generation (RAG) pipelines
→Powering AI recommendation and semantic search systems
→Enterprises needing on-prem or hybrid deployment for compliance
→AI agent platforms needing fast contextual retrieval at scale
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