See what your AI is doing. Understand why. Prove it.
Explainability for every prediction, evaluation for every agent, drift and fairness monitoring for every model, and one audit trail across all of it. It is what decides whether anything else reaches production.
- Observability is H2O.ai's fourth capability layer: explainability for every prediction, evaluation for every agent, monitoring for every model, and one audit trail across all of it.
- It is not sold on its own. Its capabilities live inside H2O Driverless AI, MLOps, Feature Store, H2O Cascade, and Eval Studio.
- AutoDoc and AutoMLI have shipped since 2017, providing regulator-grade documentation and a reason code for every prediction.
- Eval Studio runs Model Risk Management workflows for RAG and agents, on the principle that LLMs cannot be trusted to evaluate LLMs.
- Gartner found only 28% of AI use cases fully succeed and meet ROI (April 2026, n=782); explainability and evaluation are where the rest stall.
AI observability is the ability to see what your AI does, understand why, and prove it.
The pilot passes; model risk does not. Gartner found only 28% of AI use cases meet their ROI, and in regulated industries the reason is the same: nobody could explain it, evaluate it, or prove what it did.
- 01UnexplainableThe model works and nobody can say why a customer was declined. Model risk review takes weeks, then asks for the documentation again.
- 02Unevaluated"The LLM seems fine" is not evidence. Without systematic tests for hallucination, leakage, and bias, the agent is one prompt away from a headline.
- 03UndetectedYesterday's accuracy is not today's. Drift arrives silently, and the business hears about it from a customer complaint, not from a dashboard.
- 04UnaccountableWhich team, which agent, which model ran up the bill? Without attribution, AI cost is a line item that nobody owns and finance can't forecast.
One audit model across the whole platform.
Observability is a capability layer, not a separate tool you buy. Most of it lives inside the tools it governs; this page pulls it into one view.
- Explainability, since 2017AutoDoc writes the model documentation as the model trains. AutoMLI adds K-LIME, Shapley, surrogate trees, disparate impact, and a reason code per prediction.
- Evaluate RAG and agentsA modular studio for LLM and RAG evaluation across performance, reliability, security, and fairness, with PII leakage tests and radar-plot comparisons across models.
- LLMs don't grade LLMsModel Risk Management workflows: topic modeling, automated test generation, human-calibrated thresholds, failure clustering, and robustness testing with adversarial inputs.
- Monitor in productionDrift, accuracy, and fairness monitors on every deployed model, with thresholds that alert or trigger retraining, plus per-feature drift alerts from the Feature Store.
- One audit trailWho did what, when, with which model, on which data. A platform-wide audit trail, admin analytics, and model validation reports the risk team can file.
- Cost, attributedUsage and spend per user, team, model, and job. GPU hours and token cost tracked per experiment, with usage events that survive deletion.
Observability is not a line item.
An agent you cannot audit is an agent you cannot deploy.
The H2O AI Super Agent logs every plan, every tool call, and every result. Eval Studio tests it before it reaches a customer, cost attribution shows what it spent, and human approval gates sit wherever you put them.
Underneath every stage. Across the whole platform.
The rail at the bottom is Observability, drawn as one layer because that is how the model risk team experiences it: one audit trail, whichever part of the platform produced the decision. Most of it ships inside the tools it governs, on purpose.
You operate it, or H2O AI operates it for you.
Same platform either way: the same capabilities, the same governance, and the same audit trail. The choice stays open, so you can switch between them as the business changes, without rebuilding what runs on top.
H2O AI Cloud for Government
The H2O AI Cloud for Government has achieved FedRAMP High Certification, delivering the highest standard of cloud security so federal agencies can harness predictive and generative AI without compromising data sovereignty or compliance.
Explainability that clears the reviewer.
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Four more capabilities, one platform.
Watch Observability in action
Questions people ask before they call us.
AutoDoc is automatic model documentation generated as a model trains in Driverless AI or H2O-3 Secure: data, features, methods, validation, and results, in the format model risk teams expect. It has shipped since 2017 and is included with those products, not sold separately.
Bring the model your risk team rejected. Watch it clear.
We will run it through AutoDoc, AutoMLI, and Eval Studio with you, and hand your model risk team the report.