Deepchecks tests and validates machine learning models and data, covering workflows from research through production.
Open source
24 alternativesEvidently
Evidently evaluates, tests, and monitors machine learning systems and data pipelines.
About Evidently
Evidently is an open-source Python library to evaluate, test, and monitor ML and LLM systems from experiments to production. It works with tabular and text data and supports predictive and generative tasks, from classification to RAG, with 100+ built-in metrics.
It provides Reports to compute and summarize evals, Test Suites that add pass/fail conditions for regression testing and CI/CD checks, and a monitoring UI to visualize metrics over time. Reports can be viewed in Python or exported as JSON, Python dictionary, HTML, or to the monitoring UI, which can be self-hosted or used via Evidently Cloud.
Key features
- Evaluate tabular and text data for predictive and generative tasks
- 100+ built-in metrics including drift detection and LLM judges
- Create Reports with presets or custom metrics
- Add pass/fail conditions for regression and CI/CD checks
- Export reports as JSON, Python dict, HTML or monitoring UI
- Self-host monitoring UI or use Evidently Cloud
Best for: Teams building ML or LLM systems who need to evaluate experiments and monitor data pipelines.
Good for
- Open-source Python library for offline evals and live monitoring
- Python interface for custom metrics and data export
- Modular use from one-off evaluations to hosted monitoring service
Evidently at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Evidently FAQ
How do I install Evidently?
Install from PyPI with pip install evidently, or with Conda using conda install -c conda-forge evidently.
What is the difference between Reports and Test Suites?
Reports compute and summarize data, ML and LLM quality evals. A Test Suite is a Report with added pass/fail conditions for regression testing, CI/CD checks, or data validation.
What options are there for monitoring?
You can self-host the open-source monitoring UI service to visualize metrics over time, or sign up for Evidently Cloud.
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