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H2O Driverless AI

h2o.ai

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Spec score 7.8 #8 of 26 in Data Science and Machine Learning Platforms

What it is

H2O Driverless AI automates machine-learning work from feature engineering and model building through validation, tuning, selection, visualization, interpretability, and scoring-pipeline generation. It explains model output at both global and individual-prediction levels. Models can be deployed as REST endpoints, cloud services, or optimized Java code for edge devices. Supported hardware includes NVIDIA GPUs, IBM Power 9, and Intel x86 CPUs; GPU-accelerated algorithms include XGBoost, TensorFlow, and LightGBM. Documented integrations include Kubernetes, Kubeflow, SageMaker, Alteryx, Jupyter/Python, KDB, Qlik, Trifacta, and UiPath. Data sources include HDFS, Amazon S3, Azure Blob Storage, Google BigQuery, Google Cloud Storage, Snowflake, JDBC, Databricks, and Delta Table. Cloud installation options are H2O AI Cloud, AWS, Azure, and Google Cloud. H2O offers a 21-day public-cloud evaluation license and a guided two-hour cloud test drive. Commercial licensing requires contacting sales; no price is listed. The default installation disables security, so production use calls for secure installation.

Spec sheet

What it does
Driverless AI automates feature engineering, model building, visualization, and interpretability.Read 4 Oct 2026 · docs.h2o.ai
Intended users
H2O describes data scientists, DevOps and IT professionals, and business analysts as audiences for Driverless AI.Read 4 Oct 2026 · h2o.ai
Deployment
Models can be deployed as REST endpoints, cloud services, or optimized Java code for edge devices.Read 4 Oct 2026 · h2o.ai
Trial
H2O offers a 21-day evaluation license for public cloud use and a two-hour H2O Aquarium Cloud test drive with guided tutorials.Read 4 Oct 2026 · h2o.ai
Integrations
Documented integrations include Kubernetes, Kubeflow, SageMaker, Alteryx, Jupyter/Python, KDB, Qlik, Trifacta, and UiPath.Read 4 Oct 2026 · docs.h2o.ai
Headquarters
Mountain View, California, United StatesRead 28 Sep 2026 · h2o.ai
Founded
2012Read 28 Sep 2026 · h2o.ai
Authentication
Supported authentication methods include client certificate, LDAP, local, mTLS, OpenID, and PAM; unvalidated authentication is the default.Read 4 Oct 2026 · docs.h2o.ai
Cloud availability
Cloud installation options include H2O AI Cloud, AWS, Azure, and Google Cloud.Read 4 Oct 2026 · docs.h2o.ai
Data connectors
Documented data sources include HDFS, Amazon S3, Azure Blob Storage, Google BigQuery, Google Cloud Storage, Snowflake, JDBC, Databricks, and Delta Table.Read 4 Oct 2026 · docs.h2o.ai
Explainability
It provides explanations of model output at global and individual prediction levels.Read 4 Oct 2026 · h2o.ai
Hardware and algorithms
The product supports NVIDIA GPUs, IBM Power 9 and Intel x86 CPUs, and GPU accelerated algorithms including XGBoost, TensorFlow, and LightGBM.Read 4 Oct 2026 · h2o.ai
Installation platforms
The download documentation lists Linux packages, Windows 10 Pro Docker, and macOS Docker, and cautions that Windows and macOS laptops are intended only for small exploratory datasets.Read 4 Oct 2026 · docs.h2o.ai
Model workflow
It automates model validation, tuning, selection, interpretability, and scoring pipeline generation.Read 4 Oct 2026 · h2o.ai
Security configuration
The documentation warns that security is disabled in a default installation and recommends a secure installation for production.Read 4 Oct 2026 · docs.h2o.ai

Plans and prices

H2O Driverless AI Priced on request Commercial license required; contact sales for purchase information Read 4 Oct 2026 · docs.h2o.ai

Every H2O Driverless AI plan and price →

Strong points and limits

Strong points

  • Automates validation, tuning, and model selection
  • Explains global and individual predictions
  • Supports REST, cloud, and edge deployment
  • Offers a 21-day public-cloud evaluation license

Limits

  • Default installation has security disabled
  • Windows and macOS laptops are intended for small exploratory datasets

Who it suits

H2O describes data scientists, DevOps and IT professionals, and business analysts as audiences for Driverless AI. It suits teams seeking automated model workflows and multiple deployment options, provided they account for security configuration and commercial licensing.

Verdict

Driverless AI covers model building, explanation, and deployment, with a broad set of documented integrations and data sources. Plan for secure installation in production and contact sales for commercial licensing.

Where Specifiction places it

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