Evidently AI

Evidently AI covers evaluation and observability across both halves of an AI system: the conventional ML side, where the questions are data drift and whether predictive quality has decayed, and the LLM side, where the questions are whether outputs are correct, safe and consistent. It is open source with a hosted option, and it publishes a substantial body of benchmarks, guides and courses alongside the tool.

The framing its own site uses is the right one to borrow: you cannot trust what you do not test. Models drift because the world moves, not because anything broke — the code is unchanged and the inputs have quietly shifted — which is exactly the class of failure that ordinary unit tests and error monitoring never catch, because nothing errors. Drift detection and periodic evaluation are what convert that silent decay into something you can see, and having both the classic and LLM cases in one tool matters for systems that contain both.