What types of models does it support?

It supports both LLM-based systems (like chatbots, RAG, agents) and traditional predictive ML models (classifiers, regressors) with tabular data.

How is it different from other MLOps tools?

It's focused specifically on evaluation and observability, not the full ML lifecycle. It's a lightweight library, not a monolithic platform, and covers both LLM and ML use cases.

Can I write my own custom evaluation metrics?

Yes. The library has a declarative API for defining custom checks, combining rules, classifiers, and LLM-as-a-judge prompts.

How does the synthetic data generation work?

It uses LLMs and templates to generate realistic prompts, edge-case inputs, or adversarial attacks tailored to your specific use case and guidelines.

What does the self-hosted platform add over the library?

The platform adds persistent storage for evaluation results, request tracing, centralized dashboards, project management, and collaborative workflows.

Directory/Search/AI Observability/Evidently

Evidently

Evidently is a leading open-source framework (Apache 2.0) for evaluating, testing, and monitoring data and AI systems. It provides a unified Python library with 100+ metrics to measure quality, safety, and reliability, and a self-hosted platform for production observability. Key capabilities include automated evaluation with visual reports, synthetic data generation for edge cases, and continuous monitoring with dashboards to track drift and regressions. It targets ML engineers, data scientists, and AI product teams building predictive models or complex LLM-powered systems like chatbots, RAG applications, and AI agents. The framework helps catch critical issues such as hallucinations, data leaks, and risky outputs before they impact users.

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Added on

Sep 22, 2026

LLM evaluationAI monitoringSynthetic dataOpen-sourceApache 2.0

Product information

Everything worth knowing about Evidently

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Automated Evaluation

Systematically measure AI output quality, safety, and reliability. Run evals as part of a pipeline and explore results through shareable visual reports.

Synthetic Data Generation

Create realistic, edge-case, or adversarial inputs tailored to your use case – from harmless prompts to hostile attacks.

Continuous Monitoring

Keep track of evaluation results and ongoing quality checks with a live dashboard – so you catch drift, regressions, and emerging risks early.

100+ Built-in Metrics

Use a library of 100+ in-built metrics for evaluations, or add custom ones. Combine rules, classifiers, and LLM-based evaluations.

Technical capabilities

Open-Source Python Library

Standalone Python library for running evaluations with a declarative testing API and lightweight visual interface.

Must watch videos

Open-source LLM tracing, evals and prompt optimization with Evidently

15:55

Tutorial15:55

Open-source LLM tracing, evals and prompt optimization with Evidently

Evidently library https://github.com/evidentlyai/evidently Code example: https://github.com/evidentlyai/community-examples/blob/main/tutorials/local_content_generation.ipynb In this code tutorial, we explain how to run LLM evaluations for a content generation task with automated prompt optimization. We are using open-source Evidently service, showcasing tracing, dataset management and evaluation, all launched locally.

8. Tutorial: Adversarial testing for LLM applications

13:24

Tutorial13:24

8. Tutorial: Adversarial testing for LLM applications

Code example: https://github.com/evidentlyai/community-examples/blob/main/learn/LLMCourse_Adversarial_Testing.ipynb 00:02 Introduction to adversarial testing 01:57 Code example: setup and prep 03:08 Create a mock support chatbot 03:44 Test scenario 1: Asking to give financial advice. 05:30 Create an LLM judge evaluator. 08:04 Reviewing the results. 09:02 Test scenario 2: Brand risks. 09:45 Create an LLM judge evaluator. 10:40 Reviewing the results. 11:18 Create a summary dashboard COURSE PLAYLIST Full playlist: https://www.youtube.com/playlist?list=PL9omX6impEuNTr0KGLChHwhvN-q3ZF12d Instructor: Elena Samuylova, CEO Evidently AI. EVIDENTLY Sign up for Evidently Cloud https://www.evidentlyai.com/register Support Evidently on GitHub https://github.com/evidentlyai/evidently

7. Tutorial: Building and evaluating an AI agent

17:34

Tutorial17:34

7. Tutorial: Building and evaluating an AI agent

Code example: https://github.com/evidentlyai/community-examples/blob/main/learn/LLMCourse_Agent_Evals.ipynb 00:02 Intro 00:33 Create an agent with Open AI agent library 04:00 Prepare the test dataset 05:49 Implement tracing and capture usage statistics 10:26 Load traced dataset for evaluation 11:40 Set up evaluators: Correctness, Tools use match 14:46 Generate the Report and explore the results COURSE PLAYLIST Full playlist: https://www.youtube.com/playlist?list=PL9omX6impEuNTr0KGLChHwhvN-q3ZF12d Instructor: Emeli Dral, CTO Evidently AI. EVIDENTLY Sign up for Evidently Cloud https://www.evidentlyai.com/register Support Evidently on GitHub https://github.com/evidentlyai/evidently

Who uses it

Real use cases, no hype

LLM Output Safety

Detect hallucinations, PII leaks, toxic content, and prompt injection attacks in chatbots and agents.

RAG System Quality

Measure retrieval relevance, context precision, and answer faithfulness to source documents.

ML Model Drift Detection

Monitor data drift and performance decay for predictive models in production.

Prompt Optimization

Systematically test and iterate on prompts using evaluation feedback loops.

Synthetic Data Generation

Create edge-case and adversarial test datasets for robust model evaluation.

CI/CD for AI

Run automated quality gates in your deployment pipeline to prevent regressions.

Model Card Reporting

Generate standardized evaluation reports for model documentation and compliance.

What's great

Truly open-source under Apache 2.0.
Unified framework for both LLMs and traditional ML.
Massive, active community (7500+ GitHub stars).
Lightweight Python library, easy to integrate.

Technical strengths

100+ built-in metrics for LLM and data evaluation.
Can generate synthetic test data and adversarial inputs.
Provides both standalone library and self-hosted platform.

Where it falls short

Primarily a library; full platform is self-hosted.
Learning curve for custom metric creation.
Requires setting up infrastructure for persistent monitoring.
No managed cloud service; you own the hosting.

Technical limitations

Initial config for complex, multi-pipeline integrations.
Advanced tracing features require the platform component.
LLM judge evaluations depend on the underlying model quality.

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

No. The core is an open-source Python library. They offer a self-hosted platform for dashboards, tracing, and persistent monitoring, but you deploy and manage it.

Traffic Insights

Monthly Visits

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October 2026

Growth Rate

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Dominance

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Top Countries Breakdown

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Growth Trend

Stable

Traffic has remained stable.

Pricing overview

Freemium Pricing

Evidently Pricing

Evidently uses a freemium pricing structure. Real-time rates, custom enterprise quotas, and plan details are managed directly on their official platform.

Check Pricing on Evidently

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