LLM observability
Evaluate LLM-powered products, from RAGs to AI assistants.
ML observability
Monitor data drift, data quality, and performance for production ML models.
Open-source
Open-source Python library for ML monitoring with 20m+ downloads.
Evaluate
Get ready-made reports to compare models, segments, and datasets side-by-side.
Test
Run systematic checks to detect regressions, stress-test models, or validate during CI/CD.
Monitor
Monitor production data and run continuous testing. Get alerts with rich context.
Customizable dashboards
Get a clear view of your AI product performance before and after deployment. Easily share findings with your team.
Continuous testing
Evaluate generated outputs for accuracy, safety, and quality. Set criteria and test conditions specific to your use case.
In-depth debugging
Drill down to understand individual errors. Turn bad completions into test cases to continuously improve your application.
Data drift
Detect shifts in model inputs and outputs to get ahead of issues.
Data quality
Stay on top of data quality across the ML lifecycle.
Model performance
Track model quality for classification, regression, ranking, recommender systems and more.
Collaboration
Bring engineers, product managers, and domain experts to collaborate on AI quality.
Scale
With an open architecture, Evidently fits into existing environments and helps adopt AI safely and securely.
