H2O.ai
Verified Oct 15, 2026 ActiveOpen-source machine learning plus an enterprise agentic AI platform that runs on your own hardware
Listed on EffectHub ·
Pricing
- Pricing model
- Freemium
H2O-3 and the other open-source libraries are free under Apache 2.0. The enterprise platform has no public price: the site describes one annual licence priced on compute and memory rather than per token, the pricing URL is a request-a-demo form, and no free tier or trial of the commercial products is advertised.
Use tool ↗About H2O.ai
H2O.ai maintains H2O-3, an Apache-licensed in-memory platform for distributed machine learning with GLM, gradient boosting, random forest and deep learning implementations reachable from R, Python, Scala, Java and the Flow notebook, and sells a commercial stack on top: Driverless AI for automated model building, h2oGPTe for enterprise generative AI, Document AI, and an orchestration layer it now markets as the H2O AI Super Agent covering predictive, generative and agentic workloads with observability. The commercial platform deploys on-premises, fully air-gapped, in a private cloud or as a managed cloud, which is the pitch for regulated industries. Licensing is an annual contract sized on compute and memory rather than per token, quoted by sales after a demo. The company is led by founder and CEO Sri Ambati from Mountain View, California.
- API
- Yes
- Last verified
- Oct 15, 2026
Verified sources
- Home page: H2O AI Super Agent as orchestration core for predictive, generative and agentic AI with observability; h2oGPTe, Driverless AI and Document AI; deploy on-premises, fully air-gapped, private cloud or cloud; one annual licence on compute and memory, not per-token pricing; request a live demo, no prices (15 October 2026)h2o.ai ↗
- The pricing URL is a request-a-demo form with no plans, prices, free tier or trial (15 October 2026)h2o.ai ↗
- GitHub h2oai/h2o-3: open-source, distributed, in-memory machine learning platform under Apache-2.0 with GLM, gradient boosting, random forest and deep learning, usable from R, Python, Scala, Java, JSON and the Flow web interface; about 7.5k stars (15 October 2026)github.com ↗
- Terms of use last updated 10 April 2025 give the contact address H2O.ai, 800 W. El Camino Real, Suite 270, Mountain View, California, under California law; company page names Sri Ambati as founder and CEO (15 October 2026)h2o.ai ↗
Is H2O.ai legit?
We don’t label tools safe or unsafe. Below are the records we checked ourselves, each with the date we read it.
- Domain registered
- Dec 16, 2017 (8 years ago)Registry record (RDAP), registrar Key-Systems GmbH, looked up Sep 6, 2026
- TLS certificate
- Issued by Amazon, valid until Apr 28, 2027Seen in our own connection to the site on Oct 15, 2026
- Last checked
- Oct 15, 2026The site answered
- Operated by
- H2O.ai, United StatesOur research, card verified Oct 15, 2026
Pros & cons of H2O.ai
Two products under one name: a genuinely free, mature open-source ML library that data scientists have used for a decade, and an enterprise platform that only makes sense if your models must stay inside your own data centre. Evaluate them separately. The open-source side costs nothing to try; the commercial side is an annual licence negotiated after a demo, and its agentic layer is young compared with the AutoML core.
Pros
- H2O-3 is Apache 2.0, distributed, and callable from R, Python, Scala, Java and a notebook UI
- Commercial platform deploys on-premises, air-gapped, private cloud or managed cloud
- One annual licence on compute and memory instead of per-token metering
- Driverless AI AutoML, h2oGPTe and Document AI share one vendor and support contract
Cons
- No public pricing or trial for the enterprise products; everything starts with a demo
- The marketing now leads with a Super Agent orchestration story that is harder to evaluate than the AutoML core
- Open-source H2O-3 and the paid platform are separate codebases, so free experiments do not simply upgrade
- Self-hosting the full platform means owning GPUs, upgrades and integration work
Best for
- Data science teams that want a free, distributed AutoML and classical ML library with R and Python bindings
- Banks, insurers and public bodies that must run generative and predictive AI air-gapped or on-premises
- Enterprises that prefer a compute-based annual licence to per-token cloud bills
Not for
- Small teams wanting a self-serve generative AI product with a card and a price list
- Workloads that are happy on a hyperscaler's managed model APIs
Watch out: The free open-source library and the paid platform are different products: budgeting for H2O.ai means a compute-sized annual contract, not a per-seat add-on to what your team already runs for free.
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