Developer
$0
- 10,000 data rows per month
- 1 GB snapshot storage
- up to 3 projects
- 2 team seats
- access to core AI evaluation features
- community support
Overview
Evidently AI is an open-source framework designed for the evaluation, testing, and continuous monitoring of AI systems. It enables users to systematically measure the quality, safety, and reliability of AI outputs. The framework can be integrated into existing pipelines to automate evaluations and generate shareable visual reports, streamlining the process of ensuring AI models perform as expected.
How it Works
The tool supports the creation of synthetic data, allowing users to generate realistic, edge-case, or adversarial inputs tailored to specific use cases, from harmless prompts to hostile attacks. Continuous monitoring is facilitated through a live dashboard that tracks evaluation results and ongoing quality checks, helping to identify data drift, regressions, and emerging risks early in the development and deployment lifecycle.
Use Cases
- LLM-powered systems: Evaluate chatbots, RAG applications, AI agents, and copilots using customizable templates.
- Predictive ML systems: Monitor machine learning models with built-in metrics for predictive performance, data drift, and data quality.
Users can design custom AI quality systems by utilizing a library of over 100 in-built metrics or by adding their own custom evaluations, combining rules, classifiers, and LLM-based assessments.
Key features
- Open-source AI evaluation framework
- Evaluate, test, and monitor LLMs, RAG, AI agents, and ML models
- Automated evaluation with shareable visual reports
- Synthetic data generation for testing
- Continuous monitoring with live dashboards
- 100+ in-built metrics and custom evaluation options
- Detects hallucinations, data leaks, and data drift
Who uses it
AI teams, MLOps Engineers, Data Scientists, and Machine Learning Engineers use Evidently AI to ensure their models are production-ready.
Pros
- Provides a comprehensive open-source framework for AI evaluation and observability.
- Supports testing and monitoring for LLMs, RAG applications, AI agents, and traditional ML models.
- Offers automated evaluation, synthetic data generation, and continuous monitoring capabilities.
- Includes over 100 in-built metrics and allows for custom evaluation design.
- Helps detect critical AI failures such as hallucinations, data leaks, and data drift.
Cons
- Requires technical expertise for effective setup, integration, and ongoing management.
- Does not offer a managed cloud service, necessitating self-hosting and infrastructure maintenance.
- The tool's primary focus is on technical users, potentially limiting accessibility for non-technical stakeholders.
Summary
Evidently AI offers a robust open-source solution for ensuring the reliability and safety of AI systems in production. It provides essential tools for developers and data scientists to test, monitor, and debug their machine learning and LLM applications effectively.
Pricing plans
Pro
$80/month
- 100,000 data rows per month
- 100 GB snapshot storage
- up to 10 projects
- 5 team seats
- all core features
- email support
Pricing details are provided for reference and may differ from the vendor's current plans.
Website Analytics
Traffic and engagement estimates for Evidently AI. Updated Nov 4, 2026.
Monthly visits
150K
Estimated monthly website traffic
Growth
-7.2K
Month-over-month change
Growth rate
-4.6%
Relative monthly change
Bounce rate
45.3%
Estimated bounce rate
Visit history
Engagement
- Avg visit duration
- 43m 48s
- Pages per visit
- 1.6
Traffic sources
- Search Organic51.2%
- Direct21.8%
- Referrals18.3%
- Social Organic6.1%
- Gen Ai1.7%
- Mail0.6%
- Display Ads0.3%
Top countries
- United States of America20.1%
- India11.9%
- United Kingdom of Great Britain and Northern Ireland4.0%
- Brazil3.0%
- Germany3.0%
Figures are third-party estimates and may differ from actual site analytics.
Comments
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