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Deepnote vs H2O Driverless AI

Deepnote

deepnote.com

H2O Driverless AI

h2o.ai

Side by side

FigureDeepnoteH2O Driverless AI
Price from$3,920/mo—
Free planYes—
Free trialYesYes
Open source——
WebYesYes
WindowsYesYes
macOSYesYes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published31
Spec score · Data Science and Machine Learning Platforms8.4 (#5)7.8 (#8)
Founded20192012
IntegrationsThe maker lists built-in integrations including PostgreSQL, BigQuery, Amazon S3, MySQL, Snowflake, GitHub, and dbt.Documented integrations include Kubernetes, Kubeflow, SageMaker, Alteryx, Jupyter/Python, KDB, Qlik, Trifacta, and UiPath.

Plans

Deepnote

  • FreeFree
  • Team$3,920 a month
  • EnterprisePriced on request

Deepnote pricing →

H2O Driverless AI

  • H2O Driverless AIPriced on request

H2O Driverless AI pricing →

In their own words

Deepnote

Deepnote is an AI workspace for data professionals working on data analysis, exploration, and machine learning. Its copilot can chat with data, make charts, and write code; notebooks can also become dashboards or interactive apps with live data. Teams can collaborate in notebooks, comment on blocks, and share work by link or email invitation. Notebooks can run on hourly, daily, weekly, or monthly schedules, or be deployed as APIs. Listed integrations include PostgreSQL, BigQuery, Amazon S3, MySQL, Snowflake, GitHub, and dbt. Deepnote supports Python, SQL, R, and Stata, with a hybrid execution model and notebook formats including .deepnote, .ipynb, .qmd, .py, and PDF. The free plan allows up to three editors and five projects, with seven-day revision history. Paid pricing starts at $39/user/mo (annual); a 14-day trial is listed. Deepnote says it has SOC 2 Type II certification, encrypts data at rest and in transit, and does not use customer data to improve AI or ML models.

Visit deepnote.com

H2O Driverless AI

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.

Visit h2o.ai