Directory AI & ML PlatformsAI Governance & SafetyEvidently AI

Evidently AI

Open Source

About Evidently AI

Evidently AI is released as open source under the Apache 2.0 license. It provides a library of built-in evaluation metrics along with the option to add custom checks. Results can be run and viewed through either the user interface or an API.

Used for

Measuring AI output quality, safety, and reliability as part of a pipeline
Creating synthetic test inputs including edge-case or adversarial examples
Tracking evaluation results and quality checks on a live dashboard
Evaluating chatbots, RAG applications, AI agents, and copilots with customizable templates
Monitoring machine learning models for predictive performance, data drift, and data quality

From Evidently AI's own website, read 2026-10-23. How we build listings.

How to evaluate AI Governance & Safety

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Evidently AI. It comes from our AI Governance & Responsible AI buyer guide.

25%

AI Inventory & Lifecycle Governance

Automated discovery and a registry of every model, LLM/prompt, agent, and embedded-AI feature (including shadow and third-party AI); risk tiering by use case; intake-to-retirement workflow with approvals, deployment gates, and named owners; versioning and change control

25%

Regulatory Mapping & Compliance Evidence

Out-of-the-box, maintained policy packs for the EU AI Act, NIST AI RMF, ISO 42001, SR 11-7, and sector rules (e.g. NYC LL144); control mapping and gap analysis; one underlying assessment that satisfies many frameworks; audit-ready, exportable evidence and model cards

20%

Model Monitoring & Explainability

Production monitoring for drift, performance decay, and data quality; bias and fairness testing across protected groups; explainability (e.g. SHAP / feature attribution) for tabular and NLP models; the depth of this domain is what separates observability tools from pure GRC

15%

GenAI & Agentic Oversight

LLM evaluation and guardrails (hallucination, toxicity, PII leakage, prompt-injection and jailbreak defense); agent discovery, runtime policy enforcement, and a logged trail of agent actions and the authority behind them; red-teaming and continuous evaluation of agent traces

10%

Integration & ML-Stack Fit

Connectors to your model platforms (SageMaker, Vertex AI, Databricks, Dataiku, MLflow, Bedrock), CI/CD and registries; API and policy-as-code coverage; identity (SSO/RBAC); fit with existing GRC and data-security tooling rather than yet another silo

Human-in-the-Loop & Accountability

Cross-functional workflows that reach risk, legal, and business owners (not just data scientists); reviewer sign-off and attestations; issue and exception tracking; reporting that a board or regulator can read, with a defensible audit trail

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