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Evaluation and observability for AI systems
You can’t trust what you don’t test. Make sure your AI is safe, reliable and ready — on every update.
open source
Powered by the leading open-source tool
Our platform is built on top of Evidently, a trusted open-source AI evaluation tool.
With 100+ metrics readily available, it is transparent and easy to extend.
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GitHub stars
25m+
Downloads
3000+
Community members
why ai testing matters
AI fails differently
Non-deterministic AI systems break in ways traditional software doesn’t.
Hallucinations
LLMs confidently make things up.
Edge cases
Unexpected inputs bring the quality down.
Data & PII leaks
Sensitive data slips into responses.
Risky outputs
From competitor mentions to unsafe content.
Jailbreaks
Bad actors hijack your AI with clever prompts.
Cascading errors
One wrong step and the whole chain collapses.
why ai testing matters
AI fails differently
Non-deterministic AI systems break in ways traditional software doesn’t.
Hallucinations
LLMs confidently make things up.
Edge cases
Unexpected inputs bring the quality down.
Data & PII leaks
Sensitive data slips into responses.
Risky outputs
From competitor mentions to unsafe content.
Jailbreaks
Bad actors hijack your AI with clever prompts.
Cascading errors
One wrong step and the whole chain collapses.
what we do
LLM evaluation platform
From generating test cases to delivering proof your AI system is ready.
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Automated evaluation\
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Measure output accuracy, safety, and quality. Get a clear, shareable report showing exactly where AI breaks — down to each response. 
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Synthetic data\
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Create realistic, edge-case, and adversarial inputs tailored to your use case — from harmless prompts to hostile attacks. 
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Continuous testing\
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AI testing isn’t a one-time check. Track performance across every update with a live dashboard — so you catch drift, regressions, and emerging risks early.
Explore all platform features\
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 for your AI use case
Easily design your own AI quality system. Use the library of 100+ in-built metrics, or add custom ones. Combine rules, classifiers, and LLM-based evaluations.
use cases
What do you want to test first?
Built for what you are building.
Adversarial testing
Attack your AI system — before others do. Probe for PII leaks, jailbreaks and harmful content.
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RAG evaluation
Prevent hallucinations and test retrieval accuracy in RAG pipelines and chatbots.
AI agents
Go beyond single responses — validate multi-step workflows, reasoning, and tool use.
Predictive systems
Stay on top of classifiers, summarizers, recommenders, and traditional ML models.
testimonials
Trusted by AI teams worldwide
Evidently is used in 1000s of companies, from startups to enterprise.
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."
Iaroslav Polianskii
Senior Data Scientist, Wise
Egor Kraev
Head of AI, Wise
"At Wise, Evidently proved to be a great solution for monitoring data distribution in our production environment and linking model performance metrics directly to training data. Its wide range of functionality, user-friendly visualization, and detailed documentation make Evidently a flexible and effective tool for our work. These features allow us to maintain robust model performance and make informed decisions about our machine learning systems."
Alexey Grigorev
Founder DataTalks.Club
"We've run the MLOps Zoomcamp at DataTalks.Club for 4 years for thousands of ML practitioners, with ML observability modules showcasing Evidently. It's a tool the ML engineering community consistently relies on: it regularly ranks as one of the most popular ML and LLMOps tools in our surveys. It's open-source, lightweight but powerful – just how AI/ML tooling should be."
Demetris Papadopoulos
Director of Engineering, Martech, Flo Health
"Evidently is a neat and easy to use product. My team built and owns the business' ML platform, and Evidently has been one of our choices for its composition. Our model performance monitoring module with Evidently at its core allows us to keep an eye on our productionized models and act early."
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
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."
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."
Iaroslav Polianskii
Senior Data Scientist, Wise
Egor Kraev
Head of AI, Wise
"At Wise, Evidently proved to be a great solution for monitoring data distribution in our production environment and linking model performance metrics directly to training data. Its wide range of functionality, user-friendly visualization, and detailed documentation make Evidently a flexible and effective tool for our work. These features allow us to maintain robust model performance and make informed decisions about our machine learning systems."
Alexey Grigorev
Founder DataTalks.Club
"We've run the MLOps Zoomcamp at DataTalks.Club for 4 years for thousands of ML practitioners, with ML observability modules showcasing Evidently. It's a tool the ML engineering community consistently relies on: it regularly ranks as one of the most popular ML and LLMOps tools in our surveys. It's open-source, lightweight but powerful – just how AI/ML tooling should be."
Demetris Papadopoulos
Director of Engineering, Martech, Flo Health
"Evidently is a neat and easy to use product. My team built and owns the business' ML platform, and Evidently has been one of our choices for its composition. Our model performance monitoring module with Evidently at its core allows us to keep an eye on our productionized models and act early."
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
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."
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."
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 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
Principal RSA, Databricks
"Check out Evidently: I haven't seen a more promising model drift detection framework released to open-source yet!"
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
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."
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
Principal RSA, Databricks
"Check out Evidently: I haven't seen a more promising model drift detection framework released to open-source yet!"
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."
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!"
Join 3000+ AI builders
Be part of the Evidently community — join the conversation, share best practices, and help shape the future of AI quality.
Scale
Ready for enterprise
For teams building AI at scale, we offer a custom risk assessment to map risks, define evaluation criteria, and design a production-ready testing process.
Private cloud deployment in a region of choice
Role-based access control
Dedicated support and onboarding
Support for multiple organizations
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