Evidently AI
ML monitoring and testing
Evidently AI is an open-source ML and LLM observability framework for evaluating, testing, and monitoring AI-powered systems and data pipelines. With over 100 pre-built metrics and support for both tabular data and generative AI, Evidently helps teams catch model degradation, data drift, and quality issues before they impact production.
Key Capabilities
Evidently provides comprehensive monitoring with 100+ pre-made metrics covering data drift detection, model performance tracking, data quality checks, and LLM evaluation. The framework supports real-time visualization of model performance, root cause analysis for metric changes, and early detection of model deterioration even when ground truth is delayed. Evidently Cloud adds managed features including dataset management, alerting, no-code evaluations, and user management on top of the open-source library.
Who Should Use Evidently AI
Evidently is ideal for ML engineers, data scientists, and MLOps teams who need to monitor model performance in production and catch issues early. The open-source library suits individual practitioners and small teams, while Evidently Cloud serves organizations that need managed monitoring with alerting and collaboration features.
Getting Started
Install the Evidently Python library with pip and start generating reports on your data and model predictions with just a few lines of code. The open-source library runs locally and produces interactive HTML reports. For cloud-based monitoring with alerting, sign up for a free Evidently Cloud account that supports up to 10,000 rows per month.
Pricing & Accessibility: The core Evidently Python library is free and open-source under Apache 2.0. Evidently Cloud offers a free Developer plan (10K rows/month), Pro at $50/month with email alerts, Expert from $399/month with advanced testing, and custom Enterprise plans for on-premise or high-volume deployments.
Why Consider Evidently AI: Evidently uniquely combines a powerful open-source monitoring library with an optional managed cloud service, giving teams flexibility to start free and scale up while maintaining full control over their ML observability stack.
Pros
- Open-source core library under Apache 2.0 license
- 100+ pre-built metrics for data, model, and LLM monitoring
- Free cloud tier for getting started with managed monitoring
- Supports both traditional ML and generative AI evaluation
- Early drift detection even without ground truth labels
Cons
- Cloud pricing can add up for high-volume monitoring needs
- Advanced features like adversarial testing require higher-tier plans
- Requires Python knowledge to use the open-source library effectively
Who is this for?
ML model performance monitoring in production, data drift and quality detection, LLM output evaluation and testing, automated model health alerting, pre-deployment model validation and testing
Frequently Asked Questions about Evidently AI
Is Evidently AI free to use?
The core Evidently Python library is completely free and open-source under the Apache 2.0 license. Evidently Cloud offers a free Developer tier with up to 10,000 rows per month, with paid plans starting at $50/month for additional features.
What types of models can Evidently monitor?
Evidently supports monitoring for traditional ML models (classification, regression, ranking) as well as LLM and generative AI applications. It handles tabular data, text data, and provides specialized evaluations for AI-generated outputs.
How does Evidently detect model degradation?
Evidently uses statistical tests and metrics to detect data drift, prediction drift, and performance degradation. It can identify when input features have shifted or when model predictions diverge from expected patterns, even before ground truth labels are available.
Pricing
freemium
$50/mo (Pro)
Free tier: Open-source library unlimited; Cloud free at 10K rows/month
Details
APIYes
Open SourceYes
CollaborationYes
LanguagesPython, English interface
Learning CurveModerate
Integrations
PythonMLflowAirflowGrafanaPrometheus+2 more
