AI search
Qdrant
Qdrant is an open-source vector search engine written in Rust, designed to deliver fast and scalable vector similarity search services for modern AI applications.
What is Qdrant?
Qdrant is an open-source vector search engine written in Rust, designed to deliver fast and scalable vector similarity search services for modern AI applications.By eliminating traditional wrappers and utilizing a custom storage engine called Gridstore with SIMD optimizations, it achieves exceptional performance and memory efficiency, capable of handling billions of vectors with ease.
The platform supports diverse deployment models, including fully managed Qdrant Cloud, hybrid cloud, private cloud, and edge computing scenarios, catering to enterprise needs for data control and compliance such as SOC2 and HIPAA.Beyond basic vector search, Qdrant features built-in hybrid search, metadata filtering, and reranking capabilities, making it an ideal infrastructure for building RAG systems, recommendation engines, and AI agents.
With intuitive APIs and a built-in Web UI, developers can quickly integrate and visually debug search logic.Backed by an active community and over 35,000 GitHub stars, Qdrant has become the core retrieval engine behind numerous enterprise-grade AI projects.
Key features
- Rust-Native PerfBuilt entirely in Rust with SIMD optimizations and a custom Gridstore storage engine, delivering low-latency, high-throughput vector search without external wrappers.
- Native Hybrid SeBlend keyword search (BM25, SPLADE++) and vector search (dense or sparse) in a single query to enhance retrieval relevance and accuracy.
- Efficient MetadaStore metadata in JSON and apply advanced filters like nested, text, and geo.Filters are applied during HNSW traversal, ensuring high recall with low latency.
- Real-Time IndexiIndex new data instantly without rebuilding the entire index, ensuring your AI applications always have access to the most up-to-date context.
- Memory-OptimizedReduce memory usage by up to 64x using asymmetric, scalar, and binary quantization techniques while maintaining high search quality.
- Full-Spectrum ReInfuse business logic with score boosting, achieve token-level precision with late interaction models like ColBERT, and diversify results with Maximum Marginal Relevance (MMR).
- Flexible DeploymSupports on-premise, hybrid cloud, edge computing (Qdrant Edge), and fully managed cloud services, meeting security and scaling needs from startups to enterprises.
How to use it
- Sign up for Qdrant Cloud or install the Qdrant Docker image to start the service locally.
- Connect to your Qdrant instance using official client libraries for Python, JavaScript, or other languages.
- Create a collection and define the vector dimensions along with the desired metadata payload structure.
- Convert your text, images, or other data into vector embeddings and upload them to the collection with metadata.
- Execute vector similarity searches or hybrid searches via the API, using filters to narrow down results.
- Configure reranking strategies or integrate the search results into your RAG or AI Agent applications as needed.
Where it fits
- Retrieval-AugmenProvide accurate, real-time context to Large Language Models, reducing hallucinations and improving answer quality through hybrid retrieval and metadata filtering.
- Intelligent RecoPerform real-time similarity matching based on user behavior and item features in e-commerce or content platforms to deliver personalized recommendations.
- AI Agent MemoryProvide long-term memory storage for AI agents, enabling context-aware interactions across millions of conversations without losing critical information.
- Anomaly DetectioIdentify data points that deviate from normal patterns by analyzing distributions in vector space, suitable for financial fraud detection or industrial monitoring.
Pricing and free quota
Pricing and any free quota follow the official site.
Who it is for
- AI Application Developers
- Data Engineers
- Machine Learning Researchers
- Enterprise Technical Architects
FAQ
What are the advantages of Qdrant compared to other vector databases?
Qdrant is written in Rust, offering superior performance and memory efficiency.It natively supports hybrid search (keyword + vector), complex metadata filtering, and real-time indexing without requiring index rebuilds.
Does Qdrant support on-premise deployment?
Yes, Qdrant is open-source software that can be deployed on local servers, private clouds, or hybrid environments.It also offers a fully managed cloud service.
How does it handle large-scale vector data?
Qdrant uses sharding and an efficient storage architecture to support the storage and search of billions of vectors.Quantization techniques further reduce memory footprint significantly.
Which programming languages does Qdrant support?
Qdrant provides REST and gRPC APIs, with official client libraries for Python and JavaScript.It can also be integrated with other languages via HTTP requests.