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Vektonn is a high-performance battle-tested kNN vector search engine for your data science applications. It helps you manage vectors' lifecycle and radically reduces time to market.

Vektonn has the following features:

  • Support for both dense and sparse vectors
  • Precise and approximate kNN (AkNN) algorithms
  • Scalable architecture that allows to easily handle hundreds-of-GB-worth of vector data
  • and many more.


There are three main parts of Vektonn: an API, an Index, and a Data Source.

Vektonn components

  • The API has methods for search and uploading vector data. It proxies requests to corresponding Indices and Data Sources.
  • A Data Source is where all the vectors' data being persistently stored. Currently, a Data Source is implemented using Apache Kafka.
  • An Index is an in-memory snapshot of data in Data Source. It updates asynchronously from a corresponding Data Source.

A data from a single Data Source can be spread (sharded) over several Indices to fit in RAM of hosting nodes.

A single Data Source may have several Indices defined on it with different metrics.

GitHub repositories


If you have any questions or need help with Vektonn please contact us on Slack channel.


Vektonn is licensed under Apache License 2.0, so you may freely use it for commercial purposes.