H2O.ai
H2O.ai helps enterprises build and deploy machine learning models at scale.
H2O.ai is an open-source, distributed in-memory machine learning platform that democratizes AI by enabling organizations to build, deploy, and scale machine learning models at speed. The platform supports gradient boosted machines, generalized linear models, deep learning, and industry-leading AutoML functionality that automatically evaluates algorithms and hyperparameters. Used by over 20,000 global organizations including half of the Fortune 500, H2O.ai combines open-source accessibility with enterprise subscriptions and cloud offerings, setting itself apart through highest-in-industry NPS (78) and employment of top Kaggle Grandmasters.
Problem solved
Organizations struggle to operationalize machine learning quickly and cost-effectively, lacking accessible platforms that parallelize algorithms and simplify model deployment across distributed systems.
Target customer
Enterprise organizations in financial services, healthcare, retail, and manufacturing seeking to operationalize machine learning at scale; data science teams requiring AutoML and explainable AI capabilities
Founders
S
Sri Satish Ambati
CEO & Co-Founder
Previously co-founded Platfora (acquired by Workday), directed engineering at DataStax and Azul Systems, with sabbaticals in Theoretical Neuroscience at Stanford and UC Berkeley; holds MS in Math and Computer Science from University of Memphis.
C
Cliff Click
Co-Founder
Distinguished computer scientist and machine learning expert who co-founded H2O.ai in 2012.
A
Arno Candel
Chief Technology Officer
Brings a decade of experience in supercomputing to lead H2O.ai's technical development.
Funding history
Seed
$1.72M
December 2012
Led by Unknown
· Nexus Venture Partners
Series D
$72.5M
2019
Led by Goldman Sachs
· Unknown
Series E
$100M
November 2021
Led by Commonwealth Bank of Australia
· NVIDIA, Wells Fargo, Goldman Sachs Investment Partners
Total raised:
$251M
Industries
Pricing
Freemium model: free open-source software with enterprise features starting at several thousand dollars per year. Enterprise pricing and AI Cloud platform available upon request.
Notable customers
AT&T, Allergan, Bon Secours Mercy Health, Capital One, Commonwealth Bank of Australia, GlaxoSmithKline, Hitachi, Kaiser Permanente, Procter & Gamble, PayPal, PwC, Reckitt, Unilever, Walgreens, Macy's, eBay, HEB
Integrations
Java (POJO), binary format (MOJO) for production deployment; integrations with Python and R communities
Website
Competitors
DataRobot
Focuses on automated machine learning but lacks the distributed in-memory computing architecture and deep open-source community that H2O.ai leverages.
Google TensorFlow
Broader deep learning framework but less focused on enterprise AutoML, deployment simplicity, and statistical ML algorithms compared to H2O.ai's full-stack approach.
Microsoft Azure ML
Cloud-native ML platform with enterprise integration but H2O.ai differentiates through superior NPS, stronger open-source community, and specialization in explainable AI for regulated industries.
C3.ai
Enterprise AI platform focused on specific industry applications, whereas H2O.ai provides general-purpose ML infrastructure and AutoML.
Why this matters: H2O.ai has achieved exceptional scale (20,000+ organizations, half of Fortune 500) while maintaining the highest NPS in the industry (78) and employing the world's top 20 Kaggle Grandmasters. Its 2025 achievements—FedRAMP 'In Process' designation, world record for agentic AI accuracy, and third consecutive Gartner Visionary recognition—demonstrate its positioning as a critical infrastructure for regulated industries requiring explainable, production-grade AI.
Best for: Enterprise data science teams and organizations in regulated industries (finance, healthcare, manufacturing) that need to deploy accurate, explainable ML models at scale without custom engineering.
Use cases
Call Center Cost Reduction
AT&T uses H2O.ai's GenAI to transform call center operations and reduce costs by 90%. The platform's AutoML identifies optimal algorithms for predicting call routing and agent assignment, reducing manual oversight and improving efficiency at scale.
Demand Forecasting
Retail brands like Macy's, Walgreens, and HEB use H2O.ai to forecast product demand and optimize inventory planning. The platform's distributed algorithms process massive transaction datasets in-memory, enabling rapid model iteration and accurate predictions across thousands of SKUs.
Financial Risk Assessment
Capital One and Wells Fargo leverage H2O.ai for machine learning at scale, including credit risk modeling and fraud detection. The platform's parallelized algorithms and explainability features meet regulatory requirements while maintaining production-grade performance.
Personalization at Scale
Retail and e-commerce organizations use H2O.ai to create personalized customer experiences by analyzing behavioral data across millions of customers. AutoML automatically discovers the best-performing models for recommendation and segmentation without manual tuning.
Alternatives
Databricks
Broader Apache Spark-based data engineering platform with ML capabilities, better suited for data pipeline orchestration; H2O.ai specializes deeper in distributed ML algorithm execution.
Amazon SageMaker
Cloud-locked AWS ML service with broader infrastructure integration but less specialized in open-source community and cross-cloud deployment compared to H2O.ai's agnostic architecture.
H2O competitors
Most competitors lack H2O.ai's combination of industry-leading NPS, top Kaggle talent, and proven track record across 20,000 organizations with half of Fortune 500.
FAQ
What does H2O.ai do? +
H2O.ai is an open-source, distributed in-memory machine learning platform that enables organizations to build, train, and deploy ML models at scale. It includes industry-leading AutoML that automatically evaluates algorithms and hyperparameters, supports deep learning and statistical methods, and simplifies production deployment through Java (POJO) and binary (MOJO) formats. The platform is used by over 20,000 organizations globally, including more than half of the Fortune 500.
How much does H2O.ai cost? +
H2O.ai uses a freemium model with free open-source software and enterprise features starting at several thousand dollars per year. The H2O.ai AI Cloud platform and additional enterprise services are available with custom pricing. Contact H2O.ai for specific pricing based on your organization's needs.
What are alternatives to H2O.ai? +
Alternatives include Databricks (broader Spark-based data platform), Amazon SageMaker (cloud-native AWS ML), Microsoft Azure ML (enterprise cloud ML), DataRobot (specialized AutoML), and Google TensorFlow (deep learning framework). Each has different strengths—Databricks excels at data engineering, SageMaker at cloud integration, and TensorFlow at deep learning—while H2O.ai differentiates through its open-source community, highest industry NPS (78), and distributed in-memory architecture.
Who uses H2O.ai? +
H2O.ai serves over 20,000 organizations including AT&T, Capital One, Wells Fargo, Commonwealth Bank of Australia, Walgreens, Macy's, Kaiser Permanente, GlaxoSmithKline, and PayPal. Target customers are enterprise data science teams and organizations in regulated industries (finance, healthcare, retail, manufacturing) that need to deploy accurate, explainable ML models at scale. The platform is popular among Python and R communities with over one million data scientists.
How does H2O.ai compare to DataRobot? +
Both platforms offer AutoML, but H2O.ai differentiates through its distributed in-memory computing architecture that parallelizes algorithms for superior speed and scalability, strong open-source community foundation, and higher industry NPS (78). H2O.ai is better for organizations requiring fine-grained control and deployment flexibility, while DataRobot focuses more on end-to-end automation with less technical depth.
Tags
machine learning platform
AutoML
open-source
distributed computing
model deployment
explainable AI
enterprise ML
data science