DMLC

license

Apache-2.0(OSI)

source

public(github.com/dmlc/xgboost)

core features withheld

no — committee-governed

Apache-2.0, built from the public repository, and run by a project management committee that takes sponsorship for its CI costs. No company sells an edition of it.

• https://raw.githubusercontent.com/dmlc/xgboost/HEAD/LICENSE recorded 2026-09-26

  LICENSE is the Apache License, Version 2.0, "Copyright (c) 2019 by Contributors".

• https://raw.githubusercontent.com/dmlc/xgboost/HEAD/README.md recorded 2026-09-26

  README: "XGBoost is an optimized distributed gradient boosting library"; License section "Licensed under an Apache-2 license"; sponsors listed via Open Source Collective; no paid tier mentioned.

• https://ungh.cc/repos/dmlc/xgboost/files/HEAD recorded 2026-09-26

  Repository tree of 1,453 paths with no ee, enterprise, commercial or pro directory.

• https://xgboost.readthedocs.io/en/stable/contrib/community.html recorded 2026-09-26

  Community guide: "XGBoost adopts the Apache style model and governs by merit." and "The PMC may accept 3rd-party donations and sponsorships that would defray the cost of the CI infrastructure."

Measured on monthly PyPI downloads of the xgboost package; the R, JVM and other bindings are not counted.

• https://pypistats.org/api/packages/xgboost/recent recorded 2026-09-26

  last_month 29,819,156 downloads of xgboost

XGBoost does one kind of model, boosted trees, and does it very well, at scale and on GPUs. Anything else - another learner, the comparison between them, the pipeline around them - comes from a toolkit like scikit-learn, whose estimator interface it implements.

• https://xgboost.readthedocs.io/en/stable/ recorded 2026-09-26

  Docs landing tutorials: Introduction to Boosted Trees, Learning to Rank, DART, Monotonic Constraints, Feature Interaction Constraints, Survival Analysis with Accelerated Failure Time, Categorical Data, Multiple Outputs, Random Forests in XGBoost; distributed on Kubernetes, Spark, Dask, PySpark and Ray; GPU support; scikit-learn estimator interface.