Yandex
company · RussiaScores
1 product on the map — 1 open.
Openness
5 high confidence- license
- Apache-2.0(OSI)
- source
- public(github.com/catboost/catboost)
- core features withheld
- no — Yandex publishes it as open source
Apache-2.0, built from the public repository, and published by Yandex as the owner. Yandex's pages present it as open source for anyone and mention no paid edition or hosted tier.
- https://catboost.ai/ recorded 2026-09-27
catboost.ai: "developed by Yandex researchers and engineers ... It is in open-source and can be used by anyone."; no pricing, cloud or enterprise tier.
- https://raw.githubusercontent.com/catboost/catboost/master/AUTHORS recorded 2026-09-27
AUTHORS: "source code of «CatBoost» published and distributed by YANDEX LLC as the owner".
- https://raw.githubusercontent.com/catboost/catboost/master/LICENSE recorded 2026-09-27
LICENSE opens "Copyright 2017-2026 YANDEX LLC" followed by the Apache License, Version 2.0.
- https://ungh.cc/repos/catboost/catboost/files/master recorded 2026-09-27
Repository tree of 29,822 paths with no ee, enterprise, commercial or pro directory; the only other LICENSE files are third-party ones under contrib/libs.
Adoption
4 high confidenceMeasured on monthly PyPI downloads of the catboost package; the R package and the JVM and C++ appliers are not counted.
- https://pypistats.org/api/packages/catboost/recent recorded 2026-09-27
last_month 5,004,823 downloads of catboost
Capability
2 high confidenceCatBoost is one learner, boosted trees, like XGBoost and LightGBM. What sets it apart is how it treats categorical and text columns without manual encoding and how many places a trained model can be deployed. It does not compare or choose among other kinds of models.
- https://catboost.ai/docs/en/concepts/python-reference_catboost recorded 2026-09-27
Python package reference lists CatBoost, CatBoostClassifier, CatBoostRegressor and CatBoostRanker.
- https://catboost.ai/en/docs/ recorded 2026-09-27
Docs landing: training on GPU, categorical, text and embedding features, applying models from C/C++, Java, Node.js, Rust and ClickHouse, and export to CoreML, JSON, ONNX and PMML.