Qdrant
High-Performance Vector Database for AI
An open-source, Rust-based vector database optimized for semantic search, RAG applications, and AI memory systems. Self-host with complete data control.
Core Capabilities
Semantic Search
- •HNSW graph-based ANN search
- •Hybrid vector + metadata filtering
- •Full-text search support
- •Customizable distance metrics
- •High recall accuracy
Performance
- •Rust-based for speed & safety
- •GPU acceleration support
- •Disk-based HNSW storage
- •Optimized query API
- •Billion-scale capacity
Multitenancy
- •Tiered multitenancy (v1.16)
- •User-defined sharding
- •Fallback routing
- •Tenant promotion to shards
- •SaaS-ready architecture
RAG & AI Memory
- •Semantic caching
- •Conversation memory
- •Document retrieval
- •Anomaly detection
- •Recommendation systems
Filtering & Search
- •ACORN algorithm for accuracy
- •Metadata filtering
- •Text_any conditions
- •ASCII folding support
- •Payload indexing
Developer Experience
- •Python & JavaScript SDKs
- •REST & gRPC APIs
- •Revamped Web UI
- •Inline code execution
- •Conditional update API
Why We Deploy Qdrant
Self-Hosted Control
Run Qdrant on your own infrastructure with in-memory or disk-based storage. Complete data sovereignty for sensitive AI applications.
Production Ready
Benchmarks show industry-leading performance for recall and filtering. Handles massive-scale workloads with predictable latency.
AI-Native Design
Built specifically for embeddings and semantic search. Powers RAG, recommendations, anomaly detection, and multi-modal applications.
Open Source
Fully open-source under Apache 2.0. No vendor lock-in, transparent codebase, and active community development.
Common Use Cases
Qdrant powers a wide range of AI and machine learning applications.
Ready for Vector-Powered AI?
We can help you deploy and integrate Qdrant into your AI infrastructure for semantic search and memory systems.