--- title: Documentation short_description: "Build with Qdrant: install, run, and scale a vector search engine across self-hosted, Cloud, Hybrid Cloud, and Private Cloud deployments." description: "Official Qdrant documentation for vector search and retrieval — quickstarts, deployment guides, integrations, and references for self-hosted and Qdrant Cloud." weight: 2 hideTOC: true breadcrumb: false content: - partial: "documentation/banners/banner-a" title: Qdrant Documentation description: Qdrant is an AI-native vector search and a semantic search engine. You can use it to extract meaningful information from unstructured data. linkDescription: Clone this repo now and build a search engine in five minutes. cloudButton: text: Cloud Quickstart url: /documentation/cloud-quickstart/ localButton: text: Local Quickstart url: /documentation/quickstart/ contained: true - partial: documentation/banners/banner-d developingTitle: Introducing Qdrant Edge developingDescription: Qdrant Edge is a lightweight, embedded vector search engine for in-process retrieval — no background services, minimal memory footprint, and no network required. Built for robots, kiosks, mobile devices, and any environment requiring offline-capable AI search. developingBlock: title: Run vector search anywhere, even offline button: text: Get Started url: /documentation/edge/edge-quickstart/ image: src: /img/rocket.svg alt: Rocket - partial: documentation/sections/cards-section title: Qdrant User Manual description: Learn how to manage your data, run powerful searches, and leverage inference to build AI-native applications. cardsPartial: documentation/cards/docs-cards cards: - id: 1 icon: src: /icons/outline/vectors-blue.svg alt: Vectors title: Manage Data description: Create collections, manage vectors, payloads, and storage. Learn about indexing, quantization, and multitenancy. link: url: /documentation/manage-data/ text: Read More - id: 2 icon: src: /icons/outline/search-blue.svg alt: Search title: Search description: Learn about similarity search, filtering, hybrid queries, and advanced retrieval techniques. link: url: /documentation/search/ text: Read More - id: 3 icon: src: /icons/outline/integration-blue.svg alt: Inference title: Inference description: Configure dense, sparse, and multi-vector embeddings. Use cloud-hosted embedding models directly with Qdrant. link: url: /documentation/inference/ text: Read More - partial: documentation/sections/cards-section title: Support description: Get help from the Qdrant community or contact our support team. cardsPartial: documentation/cards/docs-cards cardsPerRow: 2 cards: - id: 1 icon: src: /icons/outline/discord-purple.svg alt: Discord icon title: Community Support description: Join 6,000+ active members to learn, collaborate, and participate in Qdrant's latest activities. link: text: Join our Discord url: https://qdrant.to/discord - id: 2 icon: src: /icons/outline/support-blue.svg alt: Support icon title: Qdrant Cloud Support description: Paying customers have access to our Support team. Links to the support portal are available in the Qdrant Cloud Console. link: text: Join Qdrant url: https://qdrant.to/cloud partition: develop --- # Qdrant Documentation Qdrant is an AI-native vector search engine for storing, indexing, and searching high-dimensional vectors — powering semantic search, RAG pipelines, recommendation systems, and AI-native applications. ## Getting Started - [Local Quickstart](/documentation/quickstart/index.md) — Run Qdrant locally with Docker, connect a client SDK, and create your first collection. - [Cloud Quickstart](/documentation/cloud-quickstart/index.md) — Create a free Qdrant Cloud cluster on AWS, GCP, or Azure and query it in minutes. - [Overview](/documentation/overview/index.md) — How vector search works, the client-server architecture, and core data structures (points, vectors, payloads, collections). - [API & SDKs](/documentation/interfaces/index.md) — Connect via REST or gRPC with official client libraries for Python, JavaScript/TypeScript, Rust, Go, Java, and .NET. ## Develop - [Manage Data](/documentation/manage-data/index.md) — Create collections, insert and update points and payloads, configure vector indexes, quantization, and multitenancy. - [Search](/documentation/search/index.md) — Similarity search, filtering, hybrid and multimodal queries, multi-stage pipelines, and relevance tuning. - [Inference](/documentation/inference/index.md) — Configure dense, sparse, and multi-vector embeddings; use cloud-hosted embedding models directly within Qdrant. - [Qdrant Edge](/documentation/edge/index.md) — Lightweight embedded vector search for in-process, offline-capable retrieval on robots, kiosks, and mobile devices. ## Deploy - [Deploy Overview](/documentation/deploy-intro/index.md) — Compare all Qdrant deployment options: Managed Cloud, Hybrid Cloud, Private Cloud, and self-hosted. - [Installation](/documentation/installation/index.md) — Install Qdrant via Docker, Kubernetes, or binary on Linux, macOS, or Windows. - [Managed Cloud](/documentation/cloud/index.md) — Qdrant as a managed service on AWS, GCP, or Azure with automatic scaling, backups, and zero-downtime upgrades. - [Hybrid Cloud](/documentation/hybrid-cloud/index.md) — Deploy into your own Kubernetes cluster while managing through Qdrant Cloud. - [Private Cloud](/documentation/private-cloud/index.md) — Fully air-gapped deployment in your own Kubernetes cluster with no Qdrant Cloud connectivity required. - [Distributed Deployment](/documentation/scaling/distributed_deployment/index.md) — Multi-node clusters with horizontal sharding and replication for scale and fault tolerance. - [Security](/documentation/security/index.md) — API keys, JWT-based collection-scoped access control, TLS encryption, and network binding. - [Configuration](/documentation/ops-configuration/index.md) — Customize Qdrant via config files and environment variables; runtime administration tools; GPU-accelerated vector indexing. - [Monitoring & Telemetry](/documentation/ops-monitoring/index.md) — Monitor Qdrant with Prometheus and Grafana via built-in OpenMetrics endpoints. - [Optimization](/documentation/ops-optimization/index.md) — Tune for high-speed search, high precision, or low memory usage; understand how the background optimizer works. - [Production Checklist](/documentation/production-checklist/index.md) — Pre-launch review of sharding, replication, quantization, load balancing, and observability. - [Capacity Planning](/documentation/capacity-planning/index.md) — Estimate RAM and disk for vectors, payloads, indexes, and replication factors. - [Snapshots](/documentation/snapshots/index.md) — Back up and restore collections with snapshots for disaster recovery and cross-cluster replication. - [Troubleshooting](/documentation/common-errors/index.md) — Diagnose common runtime errors: open-file limits, filesystem incompatibilities, corrupted collection metadata. ## Ecosystem - [Frameworks](/documentation/frameworks/index.md) — Integrations with 40+ AI agent and RAG frameworks: LangChain, LlamaIndex, Haystack, CrewAI, AutoGen, Spring AI, and more. - [Embedding Providers](/documentation/embeddings/index.md) — Connect to 30+ providers: OpenAI, Cohere, Jina, Mistral, AWS Bedrock, Voyage AI, Ollama, and more. - [Platforms](/documentation/platforms/index.md) — No-code and low-code integrations with n8n, Make, MuleSoft, Pipedream, and more. ## Tutorials & Examples - [Tutorials](/documentation/tutorials-lp-overview/index.md) — Hub for all tutorials covering basics, search engineering, retrieval quality, operations, migrations, and ecosystem integrations. - [Examples](/documentation/examples/index.md) — End-to-end code samples for RAG pipelines, hybrid search, multitenancy, recommendations, and multimodal search. ## Learn - [Articles](/articles/index.md) — Long-form articles on vector search, RAG, quantization, hybrid retrieval, and Qdrant internals from the engineering team. - [Qdrant Academy](/course/index.md) — Free, self-paced courses on vector search, hybrid retrieval, multivectors, and production-grade AI search applications. - [Tutorials](/documentation/tutorials-lp-overview/index.md) — Hub for all tutorials covering basics, search engineering, retrieval quality, operations, migrations, and ecosystem integrations. ## API Reference - [Qdrant API Reference](https://api.qdrant.tech/api-reference) — Full REST API reference for all Qdrant operations: collections, points, search, indexing, cluster management, and more.