# Home
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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](https://github.com/qdrant/qdrant_demo/) and build a search engine in five minutes.

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|[Cloud Quickstart](/documentation/cloud-quickstart/index.md)|[Local Quickstart](/documentation/quickstart/index.md)|


## Ready to start developing? 

***<p style="text-align: center;">Qdrant is open-source and can be self-hosted. However, the quickest way to get started is with our [free tier](https://qdrant.to/cloud) on Qdrant Cloud. It scales easily and provides an UI where you can interact with data.</p>***

[![Hybrid Cloud](/docs/homepage/cloud-cta.png)](https://qdrant.to/cloud)

## Qdrant's most popular features: 
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|[Filterable HNSW](/documentation/search/filtering/index.md) </br> Single-stage payload filtering | [Recommendations & Context Search](/documentation/search/explore/index.md#explore-the-data) </br> Exploratory advanced search| [Pure-Vector Hybrid Search](/documentation/search/hybrid-queries/index.md)</br>Full text and semantic search in one|
|[Multitenancy](/documentation/manage-data/multitenancy/index.md) </br> Payload-based partitioning|[Custom Sharding](/documentation/operations/distributed_deployment/index.md#sharding) </br> For data isolation and distribution|[Role Based Access Control](/documentation/operations/security/index.md?q=jwt#granular-access-control-with-jwt)</br>Secure JWT-based access |
|[Quantization](/documentation/manage-data/quantization/index.md) </br> Compress data for drastic speedups|[Multivector Support](/documentation/manage-data/vectors/index.md?q=multivect#multivectors) </br> For ColBERT late interaction |[Built-in IDF](/documentation/manage-data/indexing/index.md?q=inverse+docu#idf-modifier) </br> Advanced similarity calculation|

## Developer guidebooks:

| [A Complete Guide to Filtering in Vector Search](/articles/vector-search-filtering/index.md) </br> Beginner & advanced examples showing how to improve precision in vector search.| [Building Hybrid Search with Query API](/articles/hybrid-search/index.md) </br> Build a pure vector-based hybrid search system with our new fusion feature.|
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| [Multitenancy and Sharding: Best Practices](/articles/multitenancy/index.md) </br> Combine two powerful features for complete data isolation and scaling.| [Benefits of Binary Quantization in Vector Search](/articles/binary-quantization/index.md) </br> Compress data points while retaining essential meaning for extreme search performance.|