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.
Start Free in Qdrant Cloud See How It Works 25k+ GitHub Stars 60k+ Community Members Rust Powered SOC2 & HIPAA compliant WHY QDRANT?
Build for Production-Grade AI Search Engineered for real-time retrieval with the speed, accuracy, and scale that modern AI demands.
Expansive Metadata Filters Store metadata in JSON and use advanced filters, such as , , , , and more. nested text geo has_vector Learn About Metadata Filters 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 Built-in Multivector Set new standards for relevance; make the retrieval layer more expressive, flexible, and multimodal with multiple vectors per object.
See Documentation 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 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 One Engine, Endless Applications Powers AI Trip Planner on billions of reviews and images, driving revenue. 2-3x Powers Breeze AI with real-time, personalized responses and deep contextual . awareness Powers multi-agent platform with real-time context across AI-driven conversations. 2M+ Powers Dust's AI agents platform with scalable vector search across data sources. 5,000+ Powers Lyzr's AI agents, reducing latency by and increasing throughput by 90% . 150% Deploy Anywhere at Enterprise Scale Open-source DNA with enterprise-grade security and flexibility — run on-prem, hybrid, edge, or move seamlessly to Qdrant Cloud.
Qdrant Cloud Fully managed with high availability and auto-sharding on AWS, GCP, or Azure.
Explore Qdrant Cloud Qdrant Hybrid Cloud Bring your own Kubernetes with decoupled control and data planes.
Scale anywhere with full data control.
Learn about Hybrid Cloud Qdrant Private Cloud Maximum control with air-gapped, compliant deployments.
Explore Private Cloud Qdrant Edge (Beta) Lightweight, low-latency vector search close to where data is generated.
Discover Qdrant Edge Enterprise-ready tooling Deploy on any cloud, hybrid, or edge environment with full data control.
Choose the setup that fits your infrastructure and scale securely without compromise.
GDPR-aligned Options Prometheus
Datadog SSO (SAML/OIDC) 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.
ARCHITECTURE FOR THE AI - NOT KEYWORD - ERA Performance by Design We research, engineer, and optimize each component from first principles for the fastest, most scalable, and most customizable AI retrieval and search engine.
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.
Engineered for Builders Intuitive APIs and built-in tools — crafted for developers who demand more.
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 Built-In Web UI & Visualizations Explore collections, test vector and metadata queries, apply filters, and inspect results — all from a clean visual interface.
