Evidently AI

Evidently AI is a robust testing and evaluation platform designed to ensure the safety, reliability, and performance of AI products. Recognizing that AI systems fail in unique ways compared to traditional software—from LLM hallucinations and data leaks to jailbreaks and cascading errors—Evidently provides a comprehensive stack to test, evaluate, and monitor both Large Language Models (LLMs) and traditional Machine Learning (ML) models.

The platform is built upon a trusted open-source tool with over 6,000 GitHub stars, offering transparency and extensibility. It empowers AI teams to move beyond simple accuracy metrics and build a holistic AI quality system. Whether you are developing a RAG pipeline, an AI agent, or a predictive classifier, Evidently provides the necessary tools to validate every component of your system.

How to use Evidently AI

Evidently AI offers a flexible workflow that can be adapted to different development and operational needs. Users can interact with the platform in two primary ways:

1. Local Evaluation with Python SDK: Data scientists and MLOps engineers can use the open-source Evidently Python library to run evaluations directly within their existing infrastructure. This is ideal for integrating regression tests into CI/CD pipelines or for local data analysis. After running tests, users can upload the aggregated reports (JSON files) to the Evidently Cloud for visualization, tracking, and collaboration without sending raw data.
2. Cloud-Based Evaluation: For a more integrated experience, users can upload raw data, traces, or logs directly to the Evidently Cloud platform. From there, they can trigger evaluations using a no-code interface, design monitoring dashboards, set up alerts, and manage test datasets. This approach is particularly useful for debugging LLM applications where access to raw logs is crucial.

The platform also supports integrations with popular MLOps tools like MLflow, Prefect, and FastAPI, allowing for seamless incorporation into existing ML serving and monitoring blueprints.

Core Features of Evidently AI

• Comprehensive Evaluation Metrics: Access over 100 built-in metrics for data quality, data drift, and model performance (for both classification and regression). This includes specialized metrics for text data and embeddings.
• LLM-as-a-Judge: Utilize powerful LLMs to evaluate the quality of generative AI outputs. The platform provides templates for assessing criteria like factuality, adherence to guidelines, tone, and retrieval quality, which can be customized with simple text prompts.
• Synthetic Data Generation: Create diverse and realistic test cases, including edge cases and adversarial inputs, tailored to your specific use case. This helps proactively identify system vulnerabilities.
• Continuous Testing and Monitoring: Track model and data performance across every update with live, interactive dashboards. This allows for early detection of performance regressions, data drift, and emerging risks.
• Adversarial & Safety Testing: Systematically attack your AI system to probe for vulnerabilities like PII leaks, harmful content generation, and susceptibility to jailbreak prompts.
• RAG and AI Agent Testing: Go beyond single-response evaluation to validate multi-step workflows. Test the retrieval accuracy in RAG systems and assess the reasoning, tool use, and goal achievement of AI agents.
• Alerting and Reporting: Set up automated alerts for failed tests or metric threshold breaches. Generate clear, shareable reports that pinpoint exactly where and why the AI system breaks down.

Use Cases for Evidently AI

Evidently AI is trusted by thousands of companies, from startups to enterprises like DeepL, Wise, and Realtor.com.

• RAG Evaluation: Teams building chatbots and knowledge systems use Evidently to test retrieval accuracy, prevent hallucinations, and ensure the quality of generated answers.
• Adversarial Testing: Security-conscious teams use the platform to simulate attacks, ensuring their AI applications do not leak sensitive data or produce unsafe outputs.
• AI Agent Validation: Developers of complex AI agents use Evidently to validate multi-step reasoning, tool usage, and overall task success through simulated interactions.
• Predictive System Monitoring: MLOps teams rely on Evidently to monitor traditional ML models (e.g., classifiers, summarizers, recommenders) in production, tracking data drift and model performance to maintain reliability.
• Data Quality Assurance: Data scientists use Evidently reports during exploratory data analysis (EDA) and as part of CI/CD pipelines to identify unstable features and prevent data quality issues from affecting models.

Advantages of Evidently AI

Evidently AI stands out with its combination of open-source transparency and enterprise-grade capabilities.

• Hybrid Approach: Supports both LLMs and traditional ML models in a single platform.
• Open-Source Core: The foundation is a well-regarded, community-vetted open-source library, ensuring transparency and flexibility.
• Comprehensive Tooling: Provides an end-to-end solution from test data generation to continuous production monitoring.
• User-Friendly: Offers both a Python SDK for developers and a no-code UI for broader team collaboration.
• Actionable Insights: Focuses on delivering clear reports and dashboards that help teams quickly debug and improve their AI systems.

Pricing and Plans

Evidently AI offers a tiered pricing model to scale with user needs:

• Developer Plan (Free): Includes all core evaluation features, 10,000 data rows/month, 30-day data retention, and community support. Ideal for hobby projects and initial experiments.
• Pro Plan ($50/month): Builds on the free plan with alerting, 100,000 data rows/month, 12-month retention, 5 seats, and email support. Suited for refining and monitoring production AI systems.
• Expert Plan (from $399/month): Adds advanced features like synthetic data generation and adversarial testing, with 200,000 data rows/month, 10 seats, and dedicated support. Designed for testing complex AI agents and applications.
• Enterprise Plan (Custom): Offers all features with custom limits, on-premise or private cloud deployment options, premium support, and SLAs for companies managing AI at scale.