landing_page/qdrant-landing/content/documentation/_index.md

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Documentation	2	true	false	partial title description linkDescription cloudButton localButton documentation/banners/banner-a Qdrant Documentation Qdrant is an AI-native vector search and a semantic search engine. You can use it to extract meaningful information from unstructured data. <a href="https://github.com/qdrant/qdrant_demo/" target="_blank">Clone this repo now</a> and build a search engine in five minutes. text url Cloud Quickstart /documentation/cloud-quickstart/ text url contained Local Quickstart /documentation/quickstart/ true partial developingTitle developingDescription developingBlock documentation/banners/banner-d Introducing Qdrant Edge 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. title button image Run vector search anywhere, even offline text url Get Started /documentation/edge/edge-quickstart/ src alt /img/rocket.svg Rocket partial title description cardsPartial cards documentation/sections/cards-section Qdrant User Manual Learn how to manage your data, run powerful searches, and leverage inference to build AI-native applications. documentation/cards/docs-cards id icon title description link 1 src alt /icons/outline/vectors-blue.svg Vectors Manage Data Create collections, manage vectors, payloads, and storage. Learn about indexing, quantization, and multitenancy. url text /documentation/manage-data/ Read More id icon title description link 2 src alt /icons/outline/search-blue.svg Search Search Learn about similarity search, filtering, hybrid queries, and advanced retrieval techniques. url text /documentation/search/ Read More id icon title description link 3 src alt /icons/outline/integration-blue.svg Inference Inference Configure dense, sparse, and multi-vector embeddings. Use cloud-hosted embedding models directly with Qdrant. url text /documentation/inference/ Read More partial title description cardsPartial cardsPerRow cards documentation/sections/cards-section Support Get help from the Qdrant community or contact our support team. documentation/cards/docs-cards 2 id icon title description link 1 src alt /icons/outline/discord-purple.svg Discord icon Community Support Join 6,000+ active members to learn, collaborate, and participate in Qdrant's latest activities. text url Join our Discord https://qdrant.to/discord id icon title description link 2 src alt /icons/outline/support-blue.svg Support icon Qdrant Cloud Support Paying customers have access to our Support team. Links to the support portal are available in the Qdrant Cloud Console. text url Join Qdrant https://qdrant.to/cloud	develop

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Documentation

Qdrant is an AI-native vector search and a semantic search engine. You can use it to extract meaningful information from unstructured data. Want to see how it works? Clone this repo now and build a search engine in five minutes.

Cloud Quickstart	Local Quickstart

Ready to start developing?

Qdrant is open-source and can be self-hosted. However, the quickest way to get started is with our free tier on Qdrant Cloud. It scales easily and provides an UI where you can interact with data.

Qdrant's most popular features:

Filterable HNSW Single-stage payload filtering	Recommendations & Context Search Exploratory advanced search	Pure-Vector Hybrid Search Full text and semantic search in one
Multitenancy Payload-based partitioning	Custom Sharding For data isolation and distribution	Role Based Access Control Secure JWT-based access
Quantization Compress data for drastic speedups	Multivector Support For ColBERT late interaction	Built-in IDF Advanced similarity calculation

Developer guidebooks:

A Complete Guide to Filtering in Vector Search Beginner & advanced examples showing how to improve precision in vector search.	Building Hybrid Search with Query API Build a pure vector-based hybrid search system with our new fusion feature.
Multitenancy and Sharding: Best Practices Combine two powerful features for complete data isolation and scaling.	Benefits of Binary Quantization in Vector Search Compress data points while retaining essential meaning for extreme search performance.