Evidently AI
Evidently AI builds open-source and hosted tools that help teams evaluate, test, and monitor AI systems. Its framework covers predictive ML models, LLM applications, RAG systems, and AI agents across development and production.
At a Glance
- Data scientists and ML engineers
- AI/ML platform and MLOps teams
- LLM application and AI-agent product teams
- Startups and enterprises deploying AI in production
- +1 more
AI Tools by Evidently AI
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Evidently
ML and LLM Monitoring Library
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Latest News
How Evidently built open-source automated prompt optimization
Evidently 0.7.17 adds open-source LLM tracing and dataset management
Evidently 0.7.11 adds open-source synthetic data generation for LLM systems
Evidently 0.6.3 adds open-source RAG evaluation and testing
Products & Services
Apache-2.0 open-source Python framework for evaluating, testing, and monitoring ML and LLM-powered systems; includes 100+ built-in metrics, reports, test suites, custom metrics, offline evaluation, and live monitoring.
Hosted or self-hosted AI testing and observability platform with tracing, raw dataset storage, evaluation-run storage, no-code evaluation workflows, dashboards, collaboration, and alerting.
Open-source Python library built on OpenTelemetry for tracing LLM application inputs, outputs, function calls, and intermediate chain steps.
GitHub Action for running LLM output-quality checks in CI/CD and automatically testing AI systems on code updates.
Market Position
Evidently positions itself as an open-source, model-, framework-, and application-agnostic AI evaluation and observability layer spanning both predictive ML and generative AI. It differentiates from narrower LLM tracing or experiment-tracking products through its combination of 100+ built-in evaluations, test suites, monitoring, tracing, synthetic data, no-code workflows, and self-hosted/open-source deployment; adjacent competitors include Arize AI, WhyLabs, Fiddler, LangSmith, Phoenix/Arize, and other ML/LLM observability platforms.
Leadership
Founders
Elena Samuylova
Co-founder and CEO; has more than a decade of experience applying machine learning in high-scale, critical production settings, including work associated with Yandex and industrial AI.
Emeli Dral
Co-founder and CTO; machine-learning and data-analysis educator and engineer, including teaching at the Yandex School of Data Analysis and Harbour.Space University.
Executive Team
Elena Samuylova
Co-founder and CEO
Machine-learning leader with 10+ years of experience deploying AI in high-scale and critical scenarios; previously associated with Yandex and industrial AI.
Emeli Dral
Co-founder and CTO
Machine-learning engineer and educator; teaches machine learning and data analysis and leads Evidently's technical direction.
Founding Story
Evidently began as an open-source project to give data scientists and ML engineers practical ways to understand model behavior, detect data and model drift, and monitor ML systems after deployment. The founders expanded that initial monitoring vision into a broader AI-quality platform as LLMs and non-deterministic AI workflows became widespread.
Business Model
Revenue Model
Open-source Apache-2.0 software is distributed free of charge; monetization is through Evidently Cloud/Platform hosted and enterprise capabilities, including collaboration, storage, no-code workflows, dashboards, alerting, and deployment options.
Pricing Tiers
Free hosted plan for getting started with Evidently Cloud.
Hosted paid plan for teams.
Enterprise plan with private-cloud deployment option.
Target Markets
- Data scientists and ML engineers
- AI/ML platform and MLOps teams
- LLM application and AI-agent product teams
- Startups and enterprises deploying AI in production
- Product managers, domain experts, and governance teams involved in AI quality
- Testing and monitoring LLM-powered chatbots and assistants
- RAG retrieval and answer-quality evaluation
- AI-agent and multi-step workflow observability
- LLM regression testing and CI/CD quality gates
- Prompt and model comparison and optimization
- Synthetic test-data generation
- DeepL
- Wayflyer