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Ease of use

Storing metadata (attributes)

We store not only embeddings/vectors themselves, but also their metadata (attributes), which allows you to use real-world entities from your domain. For example, attributes can be a real-world identifiers (such as name) or really any custom data you want to store along with vectors.

Dense and sparse vector support

You can work with vectors of any type — dense or sparse. For example, to solve word processing problems, you can use bag-of-words and load appropriate sparse vectors into Vektonn.

All set and ready to go

We have SDKs for Python and for .NET along with published Docker images on DockerHub — everything you'll need for a quick start.

Performance and scalability

Low overhead

We have a very thin and efficient management layer atop of actual binary indices, so the overhead is pretty low.


For horizontal scaling, you can specify the attributes by which the vectors will be distributed into groups (or index shards). When processing a search query, the results from multiple shards will be automatically combined.

Data filtering (splitting)

Each shard can be further split into logical parts for an even more efficient search. Just specify split attributes in the indexing scheme, and all the queries for that index will filter out unnecessary data before searching. For example, you may efficiently search for some goods in a particular store, or for books written in a specific language.

Data lifecycle management

Online changes

We support changing indices as new data arrives (delete, update, or insert data to the index), concurrently with search queries.

Seamless versioning

You can deploy multiple indices over a single data source (containing the same vectors and attributes) and seamlessly transition to their new versions. Different indices may have different configuration parameters.