Introduction
The platform for cross-functional teams to validate AI quality in both development and production
The platform for cross-functional teams to validate AI quality in both development and production
Confident AI is the AI Quality platform that helps teams ship reliable AI applications. We provide evals in development to catch issues before deployment, and observability in production to continuously monitor AI quality at scale.
With Confident AI, teams can:
Whether you’re building RAG pipelines, agentic workflows, chatbots, or fine-tuning models — Confident AI gives engineers, QAs, PMs, and domain experts the tools to measure, improve, and maintain AI quality across the entire lifecycle, for both functionality and safety.
DeepEval is one of the most widely adopted LLM evaluation frameworks in the world, with over 13k stars, 3 million monthly downloads, and 20 million daily evaluations.
It is used by companies such as OpenAI, Google, and Microsoft.
Confident AI approaches AI quality through two complementary workflows:
Iterate rapidly on your AI application.
Data-driven iteration, not guesswork.
Full visibility into every AI execution.
See exactly what your AI is doing.
You can start with either component. Many teams begin with tracing to understand their production traffic, then build datasets from real examples for systematic testing.
Confident AI’s capabilities differs based on who you are:
Best for: Teams ready to systematically test AI quality before deployment
Establish quality gates that prevent bad AI from reaching users
Best for: Teams that want to monitor AI quality in real-time and build datasets from production
Understand how your AI actually performs in the wild
DeepEval is the open-source evaluation framework that powers the metrics and testing logic. Confident AI is the platform layer that adds collaboration, visualization, dataset management, production tracing, and team workflows on top.
Think of DeepEval as the engine, and Confident AI as the full vehicle — you get dashboards, experiment tracking, human-in-the-loop workflows, and production observability all in one place.
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All types of LLM use cases are supported, including summarization, Text-SQL, customer support chatbots, internal RAG QAs, conversational agents, and more.
These can be any architecture — RAG pipelines, agentic workflows, conversational chatbots, or combinations like RAG chatbots and agentic RAG systems.
Confident AI has tailored metrics and capabilities for different application types. Your evaluation strategy should match your use case. Learn more about supported use cases here.
Complex agentic systems are fully supported through LLM tracing. Tracing gives you visibility into every step of agent execution — tool calls, reasoning chains, and intermediate outputs.
One important consideration: be intentional about what you evaluate. Trying to measure everything often means you’re measuring nothing useful. Focus on the outputs and behaviors that matter most for your users.
Our platform is designed for cross-functional AI teams:
Yes. We offer SSO, team-based data segregation, customizable user roles and permissions, and self-hosted deployment options for your cloud environment.
We’re HIPAA compliant and sign BAAs with customers on the Premium plan or above.
Yes. While most teams use our SaaS offering, you can deploy Confident AI in your own cloud (AWS, Azure, GCP) via Docker. We integrate with your identity providers (Azure AD, Okta, Ping, etc.) for authentication. Setup typically takes 1-2 weeks.
No credit card required to start. We offer transparent pricing across 4 tiers, including a generous free tier. View pricing here.
We want you to experience value before you pay. If something doesn’t feel right, email [email protected] and we’ll make it work.