--- source_url: "https://qdrant.tech/?utm_source=openai" title: Qdrant - Vector Search Engine mirrored_at: 2026-08-04T01:03:26.989Z host: qdrant.tech cited_in_42a: true mirror_canonical: "https://index.42a.ai/qdrant.tech/index__q__utm_source_openai" --- > **Original source:** https://qdrant.tech/?utm_source=openai ## High-Performance Vector Search at Scale Qdrant helps you build the AI retrieval you want. Ship high performance, full-feature vector search at any scale and with any deployment model. ![](https://qdrant.tech/img/home/hero/blue-haze.svg) ![Qdrant astronauts exploring a planetary landscape](https://qdrant.tech/img/home/hero/astronauts.png) ### Expansive Metadata Filters Store metadata in JSON and use advanced filters, such as `nested`, `text`, `geo`, `has_vector`, and more. [Learn About Metadata Filters](https://qdrant.tech/documentation/manage-data/payload/) ![Native hybrid search illustration](https://qdrant.tech/img/home/native-hybrid-search.png) ### Native Hybrid Search (Dense + Sparse) Blend keyword and vector search in one query – use dense or sparse vectors. Supports BM25, SPLADE++, and miniCOIL. [Explore Hybrid Search](https://qdrant.tech/documentation/search/hybrid-queries/) ![Multivector illustration](https://qdrant.tech/img/home/multivector.png) ### Built-in Multivector Set new standards for relevance; make the retrieval layer more expressive, flexible, and multimodal with multiple vectors per object. [See Documentation](https://qdrant.tech/documentation/tutorials-search-engineering/using-multivector-representations/) ![One-stage filtering illustration](https://qdrant.tech/img/home/one-stage-filtering.png) ### Efficient, One-Stage Filtering Filters are applied during HNSW traversal — no pre- or post-filtering. High recall with low latency, even under complex conditions. [See Documentation](https://qdrant.tech/articles/filterable-hnsw/) ![Reranking illustration](https://qdrant.tech/img/home/reranking.png) ### Full-Spectrum Reranking Infuse business logic with score boosting, achieve token-level precision with late interaction models (e.g. ColBERT), diversify results with Maximum Marginal Relevance (MMR) [See Documentation](https://qdrant.tech/documentation/tutorials-basics/reranking-hybrid-search/) Multitenancy & Granular RBAC Private Networking Zero-downtime upgrades Backups & Point-in-time restore Vector-scoped API Keys Qdrant's technical architecture and performance capabilities have proven to be exactly what we need as we scale our AI-powered features across the platform. They are an ideal partner as we standardize our vector search infrastructure to serve millions of users worldwide. ![Canva](https://qdrant.tech/img/customer-logo/canva.svg) ### Highest‑Performance Vector Search Engine Built entirely in Rust with SIMD and a custom storage engine (Gridstore) — no wrappers, no bolt-ons. Just fast, scalable vector search. ### Real‑Time Indexing Index new data instantly without rebuilding the entire index. Your vectors are searchable the moment they're added. ### Memory‑Efficient Storage Store billions of vectors with minimal memory footprint using our optimized storage architecture. ### Asymmetric, Scalar and Binary Quantization Reduce memory usage by up to 64x while maintaining search quality with advanced quantization techniques. ![High performance vector search engine benchmark chart](https://qdrant.tech/img/home/vertical-slider/performanceimage.jpg) ### Highest‑Performance Vector Search Engine Built entirely in Rust with SIMD and a custom storage engine (Gridstore) — no wrappers, no bolt-ons. Just fast, scalable vector search. ![Real-time indexing illustration](https://qdrant.tech/img/home/vertical-slider/real-time-indexing-by-qdrant.jpg) ### Real‑Time Indexing Index new data instantly without rebuilding the entire index. Your vectors are searchable the moment they're added. ![Memory efficient storage illustration](https://qdrant.tech/img/home/vertical-slider/memory-efficient-storage-by-qdrant.jpg) ### Memory‑Efficient Storage Store billions of vectors with minimal memory footprint using our optimized storage architecture. ![Scalar and binary quantization illustration](https://qdrant.tech/img/home/vertical-slider/scalar-binary-quantization-by-qdrant.jpg) ### Asymmetric, Scalar and Binary Quantization Reduce memory usage by up to 64x while maintaining search quality with advanced quantization techniques. ![Engineered for Builders](https://qdrant.tech/img/home/web-ui.png) ### Developer friendly APIs Start with a single API call — scale to advanced control over HNSW, hybrid fusion, reranking, and multi-vector retrieval, all via REST, gRPC, or official clients (Python, JavaScript, etc.). [Explore the API Docs](https://api.qdrant.tech/api-reference) ### Built-In Web UI & Visualizations Explore collections, test vector and metadata queries, apply filters, and inspect results — all from a clean visual interface. [Try Web UI](https://qdrant.tech/documentation/web-ui/) ### Native Cloud Inference Generate text and image embeddings and run vector search in Qdrant Cloud — no separate pipeline or infrastructure needed. [Learn More About Inference](https://qdrant.tech/cloud-inference/) Integrates with leading AI tools & frameworks ### RAG & GenAI Deliver context-rich answers with hybrid dense – sparse retrieval, metadata filters, and fresh updates. [Learn More](https://qdrant.tech/rag/) ![RAG and GenAI illustration](https://qdrant.tech/img/home/horizontal-slider/rag.png) ### AI Agents Build intelligent agents with persistent memory and fast similarity search for context-aware interactions. [Learn More](https://qdrant.tech/ai-agents/) ![AI Agents illustration](https://qdrant.tech/img/home/horizontal-slider/qdrant-use-caseai-agents.png) ### Semantic Search Go beyond keywords with neural search that understands intent and delivers relevant results. [Learn More](https://qdrant.tech/advanced-search/) ![Semantic Search illustration](https://qdrant.tech/img/home/horizontal-slider/advanced-search.png) ### Recommendation Systems Power personalized recommendations with real-time similarity matching across millions of items. [Learn More](https://qdrant.tech/recommendations/) ![Recommendation Systems illustration](https://qdrant.tech/img/home/horizontal-slider/recommendation-systems.png) ### Data Analysis & Anomaly Detection Detect outliers and anomalies by finding patterns that deviate from normal behavior in your data. [Learn More](https://qdrant.tech/data-analysis-anomaly-detection/) ![Data Analysis and Anomaly Detection illustration](https://qdrant.tech/img/home/horizontal-slider/anomaly-detection.png)