We are open-source
Evidently is an open-source library with over 25 million downloads, 5000+ GitHub stars, and a thriving community. It’s licensed under Apache 2.0. This gives full transparency - you can see exactly how every metric works and trust the implementation. It also delivers an intuitive API designed for a great developer experience. The Evidently Platform builds on the library with additional UI features and workflows for team collaboration. For enterprise users, we offer both Cloud and self-hosted options for full data privacy and control.Evidently is very modular
Evidently is built to adapt to your needs without lock-ins or complex setups. It’s modular and component-based, so you can start small: you don’t have to deploy a service with multiple databases just to run a single eval.- Start with local ad hoc checks.
- Want to share results? Add a UI to track evaluations over time.
- When you run evals, choose to upload raw data or only evaluation results. It’s up to you.
- Add monitoring as you are ready to move to production workflows.
100+ built-in evaluations
Evidently puts evaluations and quality testing first. Many other tools provide a system to run and log evals, but expect you to prepare the data and implement all the metrics from scratch. We ship 100+ built-in evaluations that cover many ML and LLM use cases. From ranking metrics to data drift algorithms and LLM judges, we’ve done the hard work by implementing metrics and ways to visualize them. You can also easily extend Evidently by adding custom metrics. Evidently Cloud also provides advanced testing features, including synthetic data generation and adversarial testing, allowing you to easily create and run test scenarios.Complete feature set
Why evals are core, the Evidently Platform offers a comprehensive feature set to support AI quality workflows: with tracing, synthetic data, rich dashboards, built-in alerting etc. Get the Platform overview.
Loved by community
Thousands of companies, from startups to enterprises, use Evidently. Check some of our reviews. We’re also known for openly sharing knowledge that helps developers succeed. Check out resources like LLM evaluation course, open-source ML observability course, guides, and blogs.Handles both ML and LLM
Evidently supports both ML and LLM tasks. We believe this matters even if you’re focused solely on LLMs and not training your models. Real-world AI systems are rarely just one thing, and two types of workflows overlap. For example:- an LLM-based chatbot may need classification steps like detecting user intent.
- if you are building with RAG, you are solving a ranking problem first.