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.
Expansive Metadata Filters
Store metadata in JSON and use advanced filters, such as nested, text, geo, has_vector, and more.
Native Hybrid Search (Dense + Sparse)
Blend keyword and vector search in one query – use dense or sparse vectors. Supports BM25, SPLADE++, and miniCOIL.
Built-in Multivector
Set new standards for relevance; make the retrieval layer more expressive, flexible, and multimodal with multiple vectors per object.
Efficient, One-Stage Filtering
Filters are applied during HNSW traversal — no pre- or post-filtering. High recall with low latency, even under complex conditions.
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)
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.
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.
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.
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.).
Built-In Web UI & Visualizations
Explore collections, test vector and metadata queries, apply filters, and inspect results — all from a clean visual interface.
Native Cloud Inference
Generate text and image embeddings and run vector search in Qdrant Cloud — no separate pipeline or infrastructure needed.
Integrates with leading AI tools & frameworks
RAG & GenAI
Deliver context-rich answers with hybrid dense – sparse retrieval, metadata filters, and fresh updates.
AI Agents
Build intelligent agents with persistent memory and fast similarity search for context-aware interactions.
Semantic Search
Go beyond keywords with neural search that understands intent and delivers relevant results.
Recommendation Systems
Power personalized recommendations with real-time similarity matching across millions of items.
Data Analysis & Anomaly Detection
Detect outliers and anomalies by finding patterns that deviate from normal behavior in your data.
