New! Use Evidently to evaluate LLM-powered products

Collaborative
AI observability platform

Evaluate, test, and monitor your AI-powered products.

LLM and RAGs
ML models
Data pipelines
Evidently AI Conversation topics
Evidently AI Test suites
Evidently AI Classification quality
Evidently AI Inference
open source

Powered by the leading
open-source ML monitoring library

Our platform is built on top of Evidently, a trusted open-source ML monitoring tool.
With 100+ metrics readily available, it is transparent and easy to extend.
Learn more
4500+
GitHub stars
20m+
Downloads
2500+
Community members
Components

AI quality toolkit from development to production

Start with ad hoc tests on sample data. Transition to monitoring once the AI product is live. All within one tool.
Evidently AI Reports

Evaluate

Get ready-made reports to compare models, segments, and datasets side-by-side.
Evidently AI Tests

Test

Run systematic checks to detect regressions, stress-test models, or validate during CI/CD.
Evidently AI Monitoring dashboard

Monitor

Monitor production data and run continuous testing. Get alerts with rich context.
Adherence to guidelines and format
Hallucinations and factuality
PII detection
Retrieval quality and context relevance
Sentiment, toxicity, tone, trigger words
Custom evals with any prompt, model, or rule
LLM EVALS

Track what matters to your AI product

Easily design your own AI quality system. Use the rich library of in-built metrics, or add custom ones. Combine rules, classifiers, and LLM-based evaluations.
Learn more
Predictive ML

ML observability

Data drift

No model lasts forever. Detect shifts in model inputs and outputs to get ahead of issues.
Get early warnings on model decay without labeled data.
Understand changes in the environment and feature distributions over time.
Monitor for changes in text, tabular data and embeddings.
Learn more
Evidently AI Data drift

Data quality

Great models run on great data. Stay on top of data quality across the ML lifecycle.
Automatically profile and visualize your datasets.
Spot nulls, duplicates, unexpected values and range violations in production pipelines.
Inspect and fix issues before they impact the model performance and downstream process.
Learn more
Evidently AI Data quality

Model performance

Track model quality for classification, regression, ranking, recommender systems and more.
Get out-of-the-box performance overview with rich visuals. Grasp trends and catch deviations easily.
Ensure the models comply with your expectations when you deploy, retrain and update them.
Find the root cause of quality drops. Go beyond aggregates to see why model fails.
Learn more
Evidently AI ML Model performance
testimonials

Loved by community

Evidently is used in 1000s of companies, from startups to enterprise.
Dayle Fernandes
Dayle Fernandes
MLOps Engineer, DeepL
"We use Evidently daily to test data quality and monitor production data drift. It takes away a lot of headache of building monitoring suites, so we can focus on how to react to monitoring results. Evidently is a very well-built and polished tool. It is like a Swiss army knife we use more often than expected."
Moe Antar
Moe Antar
Senior Data Engineer, PlushCare
"We use Evidently to continuously monitor our business-critical ML models at all stages of the ML lifecycle. It has become an invaluable tool, enabling us to flag model drift and data quality issues directly from our CI/CD and model monitoring DAGs. We can proactively address potential issues before they impact our end users."
Jonathan Bown
Jonathan Bown
MLOps Engineer, Western Governors University
"The user experience of our MLOps platform has been greatly enhanced by integrating Evidently alongside MLflow. Evidently's preset tests and metrics expedited the provisioning of our infrastructure with the tools for monitoring models in production. Evidently enhanced the flexibility of our platform for data scientists to further customize tests, metrics, and reports to meet their unique requirements."
Maltzahn
Niklas von Maltzahn
Head of Decision Science, JUMO
"Evidently is a first-of-its-kind monitoring tool that makes debugging machine learning models simple and interactive. It's really easy to get started!"
Dayle Fernandes
Dayle Fernandes
MLOps Engineer, DeepL
"We use Evidently daily to test data quality and monitor production data drift. It takes away a lot of headache of building monitoring suites, so we can focus on how to react to monitoring results. Evidently is a very well-built and polished tool. It is like a Swiss army knife we use more often than expected."
Moe Antar
Moe Antar
Senior Data Engineer, PlushCare
"We use Evidently to continuously monitor our business-critical ML models at all stages of the ML lifecycle. It has become an invaluable tool, enabling us to flag model drift and data quality issues directly from our CI/CD and model monitoring DAGs. We can proactively address potential issues before they impact our end users."
Jonathan Bown
Jonathan Bown
MLOps Engineer, Western Governors University
"The user experience of our MLOps platform has been greatly enhanced by integrating Evidently alongside MLflow. Evidently's preset tests and metrics expedited the provisioning of our infrastructure with the tools for monitoring models in production. Evidently enhanced the flexibility of our platform for data scientists to further customize tests, metrics, and reports to meet their unique requirements."
Maltzahn
Niklas von Maltzahn
Head of Decision Science, JUMO
"Evidently is a first-of-its-kind monitoring tool that makes debugging machine learning models simple and interactive. It's really easy to get started!"
Evan Lutins
Evan Lutins
Machine Learning Engineer, Realtor.com
"At Realtor.com, we implemented a production-level feature drift pipeline with Evidently. This allows us detect anomalies, missing values, newly introduced categorical values, or other oddities in upstream data sources that we do not want to be fed into our models. Evidently's intuitive interface and thorough documentation allowed us to iterate and roll out a drift pipeline rather quickly."
Valentin Min
Ming-Ju Valentine Lin
ML Infrastructure Engineer, Plaid
"We use Evidently for continuous model monitoring, comparing daily inference logs to corresponding days from the previous week and against initial training data. This practice prevents score drifts across minor versions and ensures our models remain fresh and relevant. Evidently’s comprehensive suite of tests has proven invaluable, greatly improving our model reliability and operational efficiency."
Javier Lopez Peña
Javier López Peña
Data Science Manager, Wayflyer
"Evidently is a fantastic tool! We find it incredibly useful to run the data quality reports during EDA and identify features that might be unstable or require further engineering. The Evidently reports are a substantial component of our Model Cards as well. We are now expanding to production monitoring."
Ben Wilson
Ben Wilson
Principal RSA, Databricks
"Check out Evidently: I haven't seen a more promising model drift detection framework released to open-source yet!"
Evan Lutins
Evan Lutins
Machine Learning Engineer, Realtor.com
"At Realtor.com, we implemented a production-level feature drift pipeline with Evidently. This allows us detect anomalies, missing values, newly introduced categorical values, or other oddities in upstream data sources that we do not want to be fed into our models. Evidently's intuitive interface and thorough documentation allowed us to iterate and roll out a drift pipeline rather quickly."
Valentin Min
Ming-Ju Valentine Lin
ML Infrastructure Engineer, Plaid
"We use Evidently for continuous model monitoring, comparing daily inference logs to corresponding days from the previous week and against initial training data. This practice prevents score drifts across minor versions and ensures our models remain fresh and relevant. Evidently’s comprehensive suite of tests has proven invaluable, greatly improving our model reliability and operational efficiency."
Ben Wilson
Ben Wilson
Principal RSA, Databricks
"Check out Evidently: I haven't seen a more promising model drift detection framework released to open-source yet!"
Javier Lopez Peña
Javier López Peña
Data Science Manager, Wayflyer
"Evidently is a fantastic tool! We find it incredibly useful to run the data quality reports during EDA and identify features that might be unstable or require further engineering. The Evidently reports are a substantial component of our Model Cards as well. We are now expanding to production monitoring."

Join 2500+ data scientists and ML engineers

Get support, contribute, and chat about AI products.
Join our Discord community
Evidently AI Collaborative platform for AI observability
Collaboration

Built for teams

Bring engineers, product managers, and domain experts to collaborate on AI quality.
UI or API? You choose. You can run all checks programmatically or using the web interface.
Easily share evaluation results to communicate progress and show examples.
Get started
Scale

Ready for enterprise

With an open architecture, Evidently fits into existing environments and helps adopt AI safely and securely.
Private cloud deployment in a region of choice
Role-based access control
Dedicated support and onboarding
Support for multiple organizations

Get Started with AI Observability

Book a personalized 1:1 demo with our team or start a free 30-day trial.
No credit card required
By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.