Who it is built for
- AI teams testing LLM-powered products
- ML engineers monitoring predictive models
- Developers building RAG applications and AI agents
Open-source evaluation and observability for AI systems
Evidently AI is an open-source framework for evaluating, testing, and monitoring LLM applications, RAG systems, AI agents, and predictive machine-learning models. Teams can use built-in or custom metrics, generate test cases, run evaluations locally or in the cloud, and track quality over time.
The decision
Start with the job, the team, and the constraints. Product fit becomes much clearer when those three line up.
Inside the product
The core product capabilities, grouped around the work they enable.
Evidently provides more than 100 built-in metrics and supports custom rule-based, classifier-based, and LLM-based evaluations.
Teams can create realistic, edge-case, and adversarial inputs tailored to an AI use case.
Evidently tracks evaluation results and ongoing quality checks in dashboards to surface drift, regressions, and emerging risks.
Create an evaluation workflow using the Evidently Python library or Cloud, then provide traces or raw data and select metrics or tests. Run the evaluations locally or in the platform and review reports or dashboards to investigate quality changes.
Plans and official links
See the entry price, free access options, company details, and direct vendor destinations in one place.
Choosing for a real workflow?
We map the workflow, connect existing systems, choose what to buy, and build what is missing.