XGBoost
XGBoost is an open-source, community-driven gradient boosting library (founded 2014, Seattle) that enables data scientists and ML engineers to train fast, scalable regression, classification, and ranking models on structured data across distributed and GPU environments.
- Company typePrivate
- Founded2014
- HeadquartersSeattle, United States
- Headcount—
- GTM typeB2B
- OfferingSoftware
What XGBoost does
XGBoost is an open-source, community-driven software project founded in 2014 that provides an optimized distributed gradient boosting library for supervised machine learning tasks including regression, classification, and ranking. The library implements parallel tree boosting (GBDT/GBM) under the Gradient Boosting framework, with core algorithms written in C++ for performance and native interfaces for Python, R, Java, Scala, Julia, and additional wrappers for C++. It scales from single-machine inference to distributed training across billions of examples on Hadoop, SGE, MPI, and cloud clusters (AWS, GCE, Azure, Yarn), and supports GPU acceleration via CUDA with multi-GPU distributed training through the NCCL library, histogram-based tree construction, and bit-compressed memory representation. Notable technical components include XGBoost4J (Java/Scala API with native Apache Spark and Flink integration), GPU-accelerated training (up to 5.57x speedup over multicore CPUs), native missing value handling, early stopping, customizable objective functions, and model inspection/visualization tooling.
The project is incubated by the Distributed Machine Learning Community (DMLC), the same organization that created MXNet, and operates without a traditional commercial revenue model. Distribution is entirely free under the Apache 2.0 license through GitHub, PyPI, CRAN, Maven, and major cloud marketplaces, with funding limited to voluntary sponsorships via Open Source Collective for continuous integration infrastructure and donated cloud computing hours. Primary users are data scientists, machine learning engineers, academic researchers, and enterprise data platform teams building production ML pipelines; XGBoost is reportedly used in more than half of winning Kaggle competition solutions and won the 2016 John M. Chambers Statistical Software Award.
XGBoost firmographics
Firmographics- Name
- XGBoost
- Legal name
- XGBoost
- Website
- https://xgboost.ai
- Company type
- Private
- Founded year
- 2014
- Operating status
- Operating
- Short description
- XGBoost is an open-source, community-driven gradient boosting library (founded 2014, Seattle) that enables data scientists and ML engineers to train fast, scalable regression, classification, and ranking models on structured data across distributed and GPU environments.
- Ownership category
- akta.pro rank
XGBoost industry classification
Industry- Product category
- Open Source Machine Learning Library
- NAICS
- Computer Systems Design and Related Services (54151)
- SIC
- Services-Prepackaged Software (7372)
- akta.pro primary industry
- End-to-End MLOps & ML Platform Suites (HDAAABAA)
- akta.pro secondary industry
- AutoML & Low-Code ML Platform Operations (HDAAABAL)
Keywords
Where XGBoost is headquartered
LocationHeadquarters
- HQ city
- Seattle
- HQ country
- United States
- HQ region
- North America
Markets served
XGBoost business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Technology or R&D, Personnel, Infrastructure, Operations
Revenue model
- Open Source Distribution (No Direct Revenue): XGBoost is a free open-source project with no direct revenue model. The project is maintained by a global community of contributors and funded through sponsorships for CI infrastructure and cloud computing hours.
- Sponsorship Funding: Accepts monetary donations via Open Source Collective for continuous integration infrastructure, and cloud computing hour donations from organizations. Sponsors receive logo placement on the sponsors page.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Freemium | Pay-as-you-go | Free Open Source (Apache 2.0 License) |
Go-to-market motion1 record
Distribution channels5 records
Marketing channels6 records
XGBoost product offering
Product offeringCore offering
XGBoost is an open-source, optimized distributed gradient boosting library designed for fast, accurate, and scalable training of machine learning models on structured/tabular data. It provides parallel tree boosting (also known as GBDT, GBM) and supports multiple programming language interfaces including Python, R, Java, Scala, Julia, and C++. The library is freely distributed under the Apache 2.0 License and is maintained by the Distributed Machine Learning Community (DMLC).
Product overview
XGBoost is a single unified open-source library providing optimized distributed gradient boosting. The product portfolio includes the core XGBoost library with native interfaces in multiple languages (C++, Python, R, Java, Scala, Julia), plus specialized offerings: XGBoost4J for JVM-based distributed environments (Spark, Flink), XGBoost GPU for CUDA-accelerated computing, and XGBoost4J-Spark for native DataFrame integration. The library solves regression, classification, ranking, and custom objective problems using parallel tree boosting algorithms.
Differentiator
Problem solved
Functional benefit
Products and services
- XGBoost Core Library
- XGBoost Python Package
- XGBoost R Package
- XGBoost4J (JVM Package)
- XGBoost GPU-Accelerated Build
Quantifiable outcome
- Up to 5.57x speedup on GPU vs multicore CPU (on Bosch dataset with 1.18M records, 968 features)
- +4 more outcomes
Companies that use XGBoost
Customer profileNamed customers2 records
Segments3 records
Ideal customer profiles2 records
XGBoost technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- No
- API docs
- API detail
Core technology
AI maturity
App detail
Integration3 records
AI capability2 records
Feature9 records
XGBoost partnerships and signals
Strategic signalPartnerships
Four partnerships are on record, tiered core.
- Apache SparkcoreXGBoost4J provides native integration with Apache Spark via RDD and DataFrame/Dataset APIs. Enables unified pipelines embedding XGBoost training within Spark data processing workflows.
- Apache FlinkcoreXGBoost4J-Flink enables distributed XGBoost training using Flink DataSet API for large-scale data processing environments.
- Distributed Machine Learning Community (DMLC)coreXGBoost is an official project of DMLC, the incubator organization that also created MXNet and other popular ML frameworks.
- NCCL (NVIDIA Collective Communications Library)coreXGBoost uses NCCL for scalable multi-GPU communication in distributed training, enabling efficient AllReduce operations across GPU clusters.
Scale indicators6 records
Recent moves6 records
Expansion highlights4 records
XGBoost competitors and assessment
Company assessmentDirect peers
- RAPIDS cuML: NVIDIA's GPU-accelerated ML library including GPU implementations of gradient boosting. Competes directly on performance for tabular ML on GPU infrastructure.
- LightGBM: Microsoft-developed gradient boosting framework using leaf-wise tree growth and histogram-based learning. Directly competes with XGBoost on tabular ML tasks and is often benchmarked head-to-head.
- CatBoost: Yandex-developed gradient boosting library with native categorical feature handling and ordered boosting. Competes with XGBoost for structured data workloads, particularly where categorical features dominate.
- H2O.ai: Commercial open-source ML platform providing gradient boosting, AutoML, and Driverless AI. Competes with XGBoost as a higher-level abstraction over boosting models for enterprise tabular ML.
Others
- Apache MXNet: Sibling project under the Distributed Machine Learning Community (DMLC). Shares governance, contributors, and ecosystem with XGBoost but focuses on deep learning rather than gradient boosting.
Broad incumbents
- scikit-learn: General-purpose Python ML library including GradientBoosting implementations. Targets the same data scientist persona as XGBoost for structured data tasks but with broader algorithm coverage.
- Apache Spark MLlib: Apache Spark's distributed ML library providing gradient boosting and other algorithms at scale. Competes for the same enterprise Spark-based ML use cases that XGBoost4J-Spark targets.
- TensorFlow: Google-developed end-to-end ML platform. Overlaps with XGBoost in the broader ML tooling ecosystem, though TensorFlow emphasizes deep learning over tabular gradient boosting.
- PyTorch: Meta-developed deep learning framework widely adopted by ML researchers. Overlaps with XGBoost's ML community but addresses different core workloads (deep learning vs. gradient boosting).
Emerging players
- Dask-ML: Scalable ML library built on Dask for parallel computing in Python. Competes with XGBoost for distributed ML workloads on Python-native data science stacks.
Market position
Strengths4 records
Weaknesses4 records
Competitive moat5 records
Key risks5 records
Key highlights6 records
Customer concentration
XGBoost social profiles
Digital presenceXGBoost financial estimates
Financial estimateRevenue estimate
Valuation estimate
XGBoost leadership team
Management profileNumber of profiles
XGBoost funding detail
Funding detailFunding overview
Funding rounds
Investors
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
XGBoost M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about XGBoost
What does XGBoost do?
XGBoost is an open-source, optimized distributed gradient boosting library designed for fast, accurate, and scalable training of machine learning models on structured/tabular data. It provides parallel tree boosting (also known as GBDT, GBM) and supports multiple programming language interfaces including Python, R, Java, Scala, Julia, and C++. The library is freely distributed under the Apache 2.0 License and is maintained by the Distributed Machine Learning Community (DMLC).
Is XGBoost a public or private company?
XGBoost is a private company. It is classified as nonprofit foundation owned and is currently operating.
When was XGBoost founded?
XGBoost was founded in 2014.
Where is XGBoost based?
XGBoost is headquartered in Seattle, United States, in the North America region.
How does XGBoost make money?
Two revenue lines are on record. Open Source Distribution (No Direct Revenue) is the primary driver. The others are sponsorship Funding.
Who are XGBoost's main competitors?
Direct peers on record are RAPIDS cuML, LightGBM, CatBoost and H2O.ai. Apache MXNet is listed as an others. Broad incumbents are scikit-learn, Apache Spark MLlib, TensorFlow and PyTorch. Dask-ML is listed as an emerging player.
Does XGBoost have an API?
No public API is recorded for XGBoost.
What industry is XGBoost in?
XGBoost's product category is Open Source Machine Learning Library. Its primary akta.pro industry code is HDAAABAA, End-to-End MLOps & ML Platform Suites, with a secondary code of HDAAABAL, AutoML & Low-Code ML Platform Operations. Its NAICS code is 54151 and its SIC code is 7372.