# Qdrant

> High-performance open-source vector database and similarity search engine designed for AI applications at massive scale.

Qdrant is a leading open-source vector database and similarity search engine designed to handle high-dimensional vectors for performance and massive-scale AI applications. Built in Rust for unmatched speed and reliability, it powers advanced search, recommendation systems, retrieval-augmented generation (RAG), data analysis, anomaly detection, and AI agents. Qdrant offers flexible deployment options including managed cloud, hybrid cloud, and private cloud solutions.

- **High-Performance Vector Search** - Purpose-built in Rust to process billions of vectors with exceptional speed and reliability, delivering enterprise-grade performance for demanding AI workloads.

- **Cloud-Native Scalability** - Enterprise-grade managed cloud with vertical and horizontal scaling, zero-downtime upgrades, and high availability across AWS, Google Cloud, and Azure.

- **Flexible Deployment Options** - Choose from fully managed cloud, hybrid cloud (bring your own Kubernetes clusters), or private cloud for maximum control and data sovereignty.

- **Cost Efficiency with Quantization** - Dramatically reduce memory usage with built-in compression options and offload data to disk for optimized resource utilization.

- **Advanced Filtering & Payload Support** - Combine vector similarity search with payload filtering for nuanced queries and multimodal data handling.

- **Easy Integration** - Integrates with all leading embeddings and frameworks, with SDKs for Python, JavaScript, Rust, Go, and more.

- **Enterprise Security** - SOC2 Type 2 certified with RBAC, SSO, granular API keys, and GDPR compliance support for regulated environments.

- **Monitoring & Observability** - Built-in support for Prometheus/OpenMetrics, Datadog, Grafana, and other third-party monitoring integrations.

To get started, deploy Qdrant locally with Docker using `docker pull qdrant/qdrant` and `docker run -p 6333:6333 qdrant/qdrant`, or sign up for Qdrant Cloud with a free 1GB cluster. The comprehensive documentation and quick start guide help developers build production-ready vector search applications for matching, searching, and recommending.

## Features
- High-dimensional vector similarity search
- Cloud-native scalability with horizontal and vertical scaling
- Built-in quantization for memory optimization
- Payload filtering and metadata support
- Multimodal data handling
- Zero-downtime upgrades
- High availability and auto-healing
- Backup and disaster recovery
- Role-based access control (RBAC)
- Single sign-on (SSO)
- Prometheus/OpenMetrics monitoring
- Datadog and Grafana integration
- REST and gRPC APIs
- Docker deployment
- Terraform support
- Multiple distance metrics
- Recommendation API
- Hybrid cloud deployment
- Private cloud deployment
- SOC2 Type 2 certification

## Integrations
AWS, Google Cloud, Azure, Docker, Kubernetes, Terraform, Prometheus, Datadog, Grafana, LangChain, LlamaIndex, OpenAI, Hugging Face

## Platforms
WEB, API, LINUX, WINDOWS, MACOS, DEVELOPER_SDK

## Pricing
Freemium — Free tier available with paid upgrades

## Links
- Website: https://qdrant.tech
- Documentation: https://qdrant.tech/documentation/
- Repository: https://github.com/qdrant/qdrant
- EveryDev.ai: https://www.everydev.ai/tools/qdrant