Make the most of your Unstructured Data

Qdrant is a vector database & vector similarity search engine. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!

Easy to Use API

Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively utilise ready-made client for Python or other programming languages with additional functionality.

Fast and Accurate

Implement a unique custom modification of the HNSW algorithm for Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results.


Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values.
Unlike Elasticsearch post-filtering, Qdrant guarantees all relevant vectors are retrieved.

Rich data types

Vector payload supports a large variety of data types and query conditions, including string matching, numerical ranges, geo-locations, and more. Payload filtering conditions allow you to build almost any custom business logic that should work on top of similarity matching.


Cloud-native and scales horizontally.
No matter how much data you need to serve - Qdrant can always be used with just the right amount of computational resources.


Effectively utilizes your resources. Developed entirely in Rust language, Qdrant implements dynamic query planning and payload data indexing. Hardware-aware builds are also available for Enterprises.

Challenges and tasks solved with Qdrant

Here are just a few examples of how Qdrant vector search database can help your Business
  • Similar Image Search

    Sometimes text search is not enough. Qdrant vector database allows you to find similar images, detect duplicates, or even find a picture by text description.

    Qdrant filters enable you to apply arbitrary business logic on top of a similarity search. Look for similar clothes cheaper than $20? Search for a similar artwork published in the last year? Qdrant handles all possible conditions!

    For the Demo, we put together a food discovery service - it will show you a lunch suggestion based on what you visually like or dislike. It can also search for a place near you.

    Similar Image Search
  • Semantic Text Search

    Full-text search does not always provide the desired result. Documents may have too few keywords, or queries might be too large.

    One way to overcome these problems is a neural network-based semantic search, which can be used in conjunction with traditional search. The neural search uses semantic embeddings to find texts with similar meaning.

    With Qdrant vector search engine, you can build and deploy semantic neural search on your data in minutes! Compare the results of a semantic and full-text search in our demo.

    Semantic Text Search
  • Recommendations

    User behavior can be represented as a semantic vector is similar way as text or images. This vector can represent user preferences, behavior patterns, or interest in the product.

    With Qdrant vector database, user vectors can be updated in real-time, no need to deploy a MapReduce cluster. Understand user behavior in real time.


Upgrade your Neural Search Stack

Qdrant vector search engine can integrate with anything, these are some of the featured technologies

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