Confident AI is an open-source platform built to evaluate, benchmark, and optimize large language models (LLMs). With its DeepEval framework, it provides a suite of metrics for testing, including regression and A/B testing. The platform supports both in-development and production environments, offering tools for managing datasets, engineering prompts, and monitoring real-time performance.
Trusted by industry leaders, Confident AI helps organizations enhance the reliability and safety of their AI systems. By providing insights into model performance and enabling continuous improvements, Confident AI is designed to be a powerful tool for teams working with LLMs.
Confident AI Review Summary Performance Score
A
Core Feature
Comprehensive LLM evaluation and optimization
Metrics
Over 14 DeepEval metrics for diverse testing needs
Dataset Management
Tools for dataset curation, annotation, and management
Observability
Real-time monitoring and tracing of LLM applications
Human Feedback Integration
Automated collection and integration of human feedback
Security & Compliance
HIPAA-compliant with options for self-hosting and enterprise readiness
Open-Source Framework
Built on the widely adopted DeepEval framework
Enterprise Adoption
Used by organizations like BCG, AstraZeneca, and Mercedes-Benz
Who is Using Confident AI?
- BCG: Uses Confident AI to evaluate and optimize LLM applications for consulting projects, ensuring model reliability.
- AstraZeneca: Employs Confident AI for validating AI models in pharmaceutical research, ensuring their performance and safety.
- Mercedes-Benz: Leverages Confident AI to assess AI systems in automotive applications, driving optimization and compliance.
- Stellantis: Uses the platform to benchmark and refine LLMs for use in automotive technologies.
- Booking.com: Utilizes Confident AI to enhance customer service AI models, improving user experiences across platforms.
- Accenture: Adopts Confident AI to evaluate AI solutions for their consulting services, enhancing model performance.
- Cisco: Implements Confident AI to assess AI models for networking solutions, ensuring optimized operations.
- Toyota: Utilizes the platform to ensure AI model performance in automotive systems, streamlining their applications.
Confident AI Key Features 14+ DeepEval metrics for LLM evaluation
Dataset curation and annotation tools
Real-time observability of LLM performance
Automated human feedback integration
Regression and A/B testing capabilities
Support for complex agentic systems
Publicly sharable testing reports
Self-hosting and enterprise deployment options
Is Confident AI Free?
Confident AI offers a tiered pricing model:
Confident AI Pricing Plans
- Free Tier � $0: Includes 1 project, 5 test runs per week, and 1-week data retention
- Starter Tier � $29.99/user/month: Full LLM testing suite, dataset management, 3 months data retention
- Premium Tier � $79.99/user/month: Advanced observability, human feedback integration, and enterprise support
Confident AI Pros & Cons
Comprehensive suite of evaluation tools for LLM applications
Integration with DeepEval provides proven metrics
Real-time monitoring and tracing capabilities
Support for complex agentic systems
Automated human feedback collection enhances model refinement
Options for self-hosting and enterprise deployment
Open-source framework fosters community collaboration
Trusted by leading organizations across various industries
Initial setup and learning curve for new users
Advanced features available only in paid tiers
Self-hosting may require additional IT resources
Primarily focused on LLM applications, limiting broader AI use cases
