[7] It works on Linux, Windows, macOS, and is available in Python,[8] R,[9] and models built using CatBoost can be used for predictions in C++, Java,[10] C#, Rust, Core ML, ONNX, and PMML.

[13] As of April 2022, CatBoost is installed about 100000 times per day from PyPI repository[14] CatBoost has gained popularity compared to other gradient boosting algorithms primarily due to the following features[15] In 2009 Andrey Gulin developed MatrixNet, a proprietary gradient boosting library that was used in Yandex to rank search results.

In 2014–2015 Andrey Gulin with a team of researchers has started a new project called Tensornet that was aimed at solving the problem of "how to work with categorical data".

In 2016 Machine Learning Infrastructure team led by Anna Dorogush started working on Gradient Boosting in Yandex, including Matrixnet and Tensornet.

They implemented and open-sourced the next version of Gradient Boosting library called CatBoost, which has support of categorical and text data, GPU training, model analysis, visualisation tools.