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

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Home	2	true	false	partial title description linkDescription cloudButton localButton documentation/banners/banner-a Qdrant Documentation Qdrant is an AI-native vector database 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/quickstart-cloud/ text url contained Local Quickstart /documentation/quickstart/ true partial developingTitle developingDescription developingBlock documentation/banners/banner-d Ready to start developing? Qdrant is open-source and can be self-hosted. However, the quickest way to get started is with our <a href="https://qdrant.to/cloud" target="_blank">free tier</a> on Qdrant Cloud. It scales easily and provides a UI where you can interact with data. title button image Create your first Qdrant Cloud cluster today text url Get Started https://qdrant.to/cloud src alt /img/rocket.svg Rocket partial title description button cardsPartial cards documentation/sections/cards-section Optimize Qdrant's performance Boost search speed, reduce latency, and improve the accuracy and memory usage of your Qdrant deployment. text url Learn More /documentation/guides/optimize/ documentation/cards/docs-cards id tag icon title description link 1 Documents src alt /icons/outline/documentation-blue.svg Documents Distributed Deployment Scale Qdrant beyond a single node and optimize for high availability, fault tolerance, and billion-scale performance. url text /documentation/guides/distributed_deployment/ Read More id tag icon title description link 2 Documents src alt /icons/outline/documentation-blue.svg Documents Multitenancy Build vector search apps that serve millions of users. Learn about data isolation, security, and performance tuning. url text /documentation/guides/multiple-partitions/ Read More id tag tagColor icon title description link 3 Blog violet src alt /icons/outline/blog-purple.svg Blog Vector Quantization Learn about cutting-edge techniques for vector quantization and how they can be used to improve search performance. url text /articles/what-is-vector-quantization/ Read More	qdrant

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Documentation

Qdrant is an AI-native vector database 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:

Filtrable 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.