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title: Qdrant Documentation
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# Documentation
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**Qdrant (read: quadrant)** is a vector similarity search engine. Use our documentation to develop a production-ready service with a convenient API to store, search, and manage vectors with an additional payload. Qdrant's expanding features allow for all sorts of neural network or semantic-based matching, faceted search, and other applications.
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Qdrant is an AI-compatible 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.
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## Product Release: Announcing Qdrant Hybrid Cloud!
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***<p style="text-align: center;">Now you can attach your own infrastructure to Qdrant Cloud!</p>***
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|[Docker Quickstart](/documentation/quick-start)|[Cloud Quickstart](/documentation/cloud/quickstart-cloud/)|
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|Use Qdrant Client SDKs|Try the GUI Dashboard|
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[](https://qdrant.to/cloud)
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## Ready to start developing?
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Use [**Qdrant Hybrid Cloud**](/hybrid-cloud/) to build the best private environment that suits your needs. Manage your own clusters via the [Qdrant Cloud UI](/documentation/cloud/), but continue to run them within your own private infrastructure for complete security and sovereignty.
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[](https://qdrant.to/cloud)
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## First-Time Users:
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***<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>***
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There are three ways to use Qdrant:
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***<p style="text-align: center;">Besides regular semantic search, we are also known for innovative features:</p>***
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1. [**Run a Docker image**](quick-start/) if you don't have a Python development environment. Setup a local Qdrant server and storage in a few moments.
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2. [**Get the Python client**](https://github.com/qdrant/qdrant-client) if you're familiar with Python. Just `pip install qdrant-client`. The client also supports an in-memory database.
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3. [**Spin up a Qdrant Cloud cluster:**](cloud/) the recommended method to run Qdrant in production. Read [Quickstart](cloud/quickstart-cloud/) to setup your first instance.
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### Recommended Workflow:
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First, try Qdrant locally using the [Qdrant Client](https://github.com/qdrant/qdrant-client) and with the help of our [Tutorials](tutorials/) and Guides. Develop a sample app from our [Examples](examples/) list and try it using a [Qdrant Docker](guides/installation/) container. Then, when you are ready for production, deploy to a Free Tier [Qdrant Cloud](cloud/) cluster.
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### Try Qdrant with Practice Data:
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You may always use our [Practice Datasets](datasets/) to build with Qdrant. This page will be regularly updated with dataset snapshots you can use to bootstrap complete projects.
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## Popular Topics:
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| Tutorial | Description | Tutorial| Description |
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|----------------------------------------------------|----------------------------------------------|---------|------------------|
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| [Installation](guides/installation/) | Different ways to install Qdrant. | [Collections](concepts/collections/) | Learn about the central concept behind Qdrant. |
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| [Configuration](guides/configuration/) | Update the default configuration. | [Bulk Upload](tutorials/bulk-upload/) | Efficiently upload a large number of vectors. |
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| [Optimization](tutorials/optimize/) | Optimize Qdrant's resource usage. | [Multitenancy](tutorials/multiple-partitions/) | Setup Qdrant for multiple independent users. |
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## Common Use Cases:
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Qdrant is ideal for deploying applications based on the matching of embeddings produced by neural network encoders. Check out the [Examples](examples/) section to learn more about common use cases. Also, you can visit the [Tutorials](tutorials/) page to learn how to work with Qdrant in different ways.
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| Use Case | Description | Stack |
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|-----------------------|----------------------------------------------|--------|
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| [Semantic Search for Beginners](tutorials/search-beginners/) | Build a search engine locally with our most basic instruction set. | Qdrant |
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| [Build a Simple Neural Search](tutorials/neural-search/) | Build and deploy a neural search. [Check out the live demo app.](https://demo.qdrant.tech/#/) | Qdrant, BERT, FastAPI |
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| [Build a Search with Aleph Alpha](tutorials/aleph-alpha-search/) | Build a simple semantic search that combines text and image data. | Qdrant, Aleph Alpha |
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| [Developing Recommendations Systems](https://githubtocolab.com/qdrant/examples/blob/master/qdrant_101_getting_started/getting_started.ipynb) | Learn how to get started building semantic search and recommendation systems. | Qdrant |
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| [Search and Recommend Newspaper Articles](https://githubtocolab.com/qdrant/examples/blob/master/qdrant_101_text_data/qdrant_and_text_data.ipynb) | Work with text data to develop a semantic search and a recommendation engine for news articles. | Qdrant |
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| [Recommendation System for Songs](https://githubtocolab.com/qdrant/examples/blob/master/qdrant_101_audio_data/03_qdrant_101_audio.ipynb) | Use Qdrant to develop a music recommendation engine based on audio embeddings. | Qdrant |
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| [Image Comparison System for Skin Conditions](https://colab.research.google.com/github/qdrant/examples/blob/master/qdrant_101_image_data/04_qdrant_101_cv.ipynb) | Use Qdrant to compare challenging images with labels representing different skin diseases. | Qdrant |
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| [Question and Answer System with LlamaIndex](https://githubtocolab.com/qdrant/examples/blob/master/llama_index_recency/Qdrant%20and%20LlamaIndex%20%E2%80%94%20A%20new%20way%20to%20keep%20your%20Q%26A%20systems%20up-to-date.ipynb) | Combine Qdrant and LlamaIndex to create a self-updating Q&A system. | Qdrant, LlamaIndex, Cohere |
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| [Extractive QA System](https://githubtocolab.com/qdrant/examples/blob/master/extractive_qa/extractive-question-answering.ipynb) | Extract answers directly from context to generate highly relevant answers. | Qdrant |
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| [Ecommerce Reverse Image Search](https://githubtocolab.com/qdrant/examples/blob/master/ecommerce_reverse_image_search/ecommerce-reverse-image-search.ipynb) | Accept images as search queries to receive semantically appropriate answers. | Qdrant |
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## Qdrant's best-known features:
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|[Filtrable HNSW](/documentation/filtering/) </br> Single-stage payload filtering | [Discovery & Context Search](/documentation/filtering/) </br> Exploratory advanced search| [Pure-Vector Hybrid Search](/documentation/hybrid-queries/)</br>Full text and semantic search in one|
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|[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 |
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|[Binary 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|
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