Evidently AI Analysis: $15M Raised
What is Evidently AI?
Evidently AI provides an open-source and enterprise platform for evaluating, testing, and monitoring AI systems across the development lifecycle. Its products cover LLM testing, RAG evaluation, adversarial red-teaming, ML monitoring, and AI agent testing, with built-in and custom metrics for safety, hallucinations, retrieval quality, and drift. The company reports 7,500+ GitHub stars, 40M+ downloads, 3,000+ community members, and use across thousands of companies.
Location
London, United Kingdom
Employees
1-10
Founded
2020
Revenue
$0-5M
Business Model
Subscription-based revenue model with potential open-core features for enterprise clients.
Product Features & Capabilities
- LLM observability, ML observability, open-source Python library for ML monitoring, customizable dashboards, continuous testing, data drift detection, data quality monitoring, model performance tracking, ready-made reports, systematic checks, in-depth debugging.
Pricing
- Developer: Free plan for hobby projects and experiments.
- All core evaluation features
- 100+ built-in metrics
- 10,000 data rows/month
- 30-day retention
- Community support
- Pro: $50/month for refining and monitoring AI systems.
- All features in Free, plus:
- Alerting
- 100,000 data rows/month
- 12-month retention
- 5 seats, 10 projects
- Email support
- Expert: From $399/month for advanced testing of AI agents and complex apps.
- All features in Pro, plus:
- Synthetic data
- Adversarial testing
- Agent simulations
- 200,000 data rows/month
- 10 seats, unlimited projects
- Dedicated support
- Enterprise: Custom pricing for companies managing AI at scale.
- All features with custom limits
- On-prem or private cloud deployment options
- Premium support and SLA
- Startups: Special offer for early-stage startups building with AI.
- All core features
- Free credits towards Pro tier
- Premium support
How much Evidently AI raised
Series A - $15M
December 9, 2024Lead Investor: DN Capital, FRAMEWORK, Clear Ventures, Fellows Fund
Who are the founders of Evidently AI
ES
Elena Samuylova
CEO and Co-founder
ED
Emeli Dral
CTO and Co-founder
Market Focus
Evidently AI primarily focuses on the artificial intelligence (AI) industry, specifically in the area of AI observability and machine learning (ML) monitoring.
Investors
- Runa Capital
- Y Combinator
- Nauta Capital
- Clear Ventures
- Fly Ventures
Competitors Overview
The main competitors of Evidently AI in the AI observability market include:
-
Fiddler AI:
- Overview: Fiddler AI provides a unified AI observability platform designed for monitoring, explaining, and improving machine learning models, particularly focusing on large language models (LLMs).
- Notable Advantages: It offers enterprise-grade safeguards, real-time monitoring, and actionable insights, which help teams enhance their AI deployments while ensuring compliance and security. Fiddler emphasizes explainable AI, allowing users to understand model decisions better.
-
Arize AI:
- Overview: Arize AI specializes in machine learning observability, enabling teams to monitor and improve AI models continuously. It helps in diagnosing issues and enhancing model performance.
- Notable Advantages: Arize AI is known for its first-mover advantage in the AI observability space, providing a comprehensive platform that integrates seamlessly with existing ML stacks. It focuses on real-time monitoring and actionable insights, which are crucial for maintaining model performance.
-
NannyML:
- Overview: NannyML is an open-source platform that allows users to monitor machine learning models in production, focusing on performance estimation and data drift detection.
- Notable Advantages: It is model-agnostic and provides an easy-to-use interface with interactive visualizations. NannyML emphasizes a performance-centric workflow, allowing users to monitor what truly matters and fix issues effectively.
These competitors differ from Evidently AI primarily in their specific features, focus areas, and user interfaces, with each offering unique advantages in the realm of AI observability.
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