MLOps & experiment tracking · Evidently AI, Inc.

Evidently AI

Open-source and cloud model-monitoring tool that checks deployed ML/LLM systems for drift, data quality, and performance issues.

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Evidently AI is a production model-monitoring and observability tool, not an experiment tracker: instead of logging training runs, it evaluates and watches deployed models and datasets for data drift, target drift, data-quality problems, and performance degradation, and it also supports evaluating LLM outputs for issues like hallucination or toxicity. Its core library is open source and generates comparison reports and statistical test suites that can run in notebooks, batch jobs, or CI/CD pipelines. Evidently Cloud adds a hosted dashboard, alerting, and team collaboration on top of the same open-source checks, and can be self-hosted for teams that want the dashboard without sending data externally. It is commonly paired with an experiment tracker (for training) and belongs alongside Arize AI, WhyLabs, and Fiddler AI in the monitoring/observability bucket rather than with MLflow or W&B.

At a glance

Vendor Evidently AI, Inc.
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Teams needing open-source-first drift and data-quality monitoring for deployed ML or LLM systems.

Pricing

The open-source library and self-hosted platform are free; Evidently Cloud has paid tiers, but no self-serve figures were shown on the vendor's pricing page in this session, and higher tiers are sold via sales conversation.

Checked on the vendor's own page on September 21, 2026: no prices are published. Expect to be quoted.

Features

  • Data and target drift detection
  • Data-quality checks and test suites
  • Model performance monitoring over time
  • LLM output evaluation (hallucination, toxicity, relevance)
  • Pre-built HTML/JSON reports and dashboards
  • CI/CD integration for automated checks
  • Open-source core with a hosted Cloud dashboard

Integrations

Profile last reviewed September 21, 2026

Alternatives

Arize AI Hosted AI observability platform that monitors deployed ML models and LLM applications for drift, quality, and performance issues. WhyLabs Formerly a hosted AI-observability platform for monitoring model and data drift; the company has since shut down and open-sourced its stack. Fiddler AI Hosted AI observability and guardrails platform for monitoring deployed ML models and LLM/agent applications.

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Terms to know

Data observability Continuous automated monitoring of pipelines and datasets to catch freshness, volume and quality problems early. Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data drift A change in the statistical properties of input data over time that can silently degrade a deployed model's accuracy. Hallucination When a language model generates confident, fluent output that is factually wrong or unsupported by its source material.

Related guides

How to choose an MLOps & experiment tracking tool "MLOps" covers five different jobs — tracking, monitoring, orchestration, serving, versioning — and most teams need more than one, not the biggest single tool.