---
title: Qdrant Documentation
weight: 10
hideTOC: true
---
# Documentation

Qdrant is an AI-native vector dabatase and a semantic search engine. You can use it to extract meaningful information from unstructured data. **[Learn more about vector search](/documentation/overview/)** and how it works with AI.

|||
|-:|:-|
|Docker Quickstart|Cloud Quickstart|
|[Use the Python Client](/documentation/quick-start/)|[Try the GUI Dashboard](/documentation/cloud/quickstart-cloud/)|

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