Introduction
The platform for cross-functional teams to validate AI quality in both development and production
What is Confident AI?
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:
- Experiment in development - Test different prompts, models, and parameters to find what works best
- Iterate on AI apps - Call your application via HTTPS or prompts to rapidly iterate and evaluate changes
- Catch regressions pre-deployment - Run automated evals in CI/CD to detect breaking changes before they reach users
- Monitor quality in production - Trace every AI execution and score quality in real-time
- Get live alerting - Receive instant notifications when AI quality degrades
- Red team for security - Test for safety vulnerabilities and harden your AI against adversarial attacks
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.
Confident AI's evals are 100% powered by DeepEval
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.
How AI Quality works
Confident AI approaches AI quality through two complementary workflows:
Iterate rapidly on your AI application.
- Call your app via HTTPS or prompts to test changes
- Compare prompts, models, and parameters
- Run 40+ metrics to measure quality
- Find the best configuration for your use case
Data-driven iteration, not guesswork.
Full visibility into every AI execution.
- Trace requests end-to-end with spans
- Capture inputs, outputs, latency, and tokens
- Debug issues with complete context
- Build datasets from real production traffic
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.
Key capabilities
Confident AI’s capabilities differs based on who you are:
- For Engineers - Unit-test AI apps in CI/CD, debug with traces, experiment with prompts and models
- For QAs - Build test datasets, run regression suites, validate AI behavior across scenarios
- For PMs - Track quality metrics over time, compare experiments, monitor production health
- For SMEs & Annotators - Label data, review AI outputs, provide human feedback at scale
Choose your quickstart
Best for: Teams ready to systematically test AI quality before deployment
- Create and annotate golden datasets
- Run regression tests to catch breaking changes
- Experiment with prompts, models, and parameters
- Integrate evals into your CI/CD pipeline
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
- Trace every AI execution with full visibility
- Run online evals to score production traffic
- Debug issues and identify quality regressions
- Build datasets from real user interactions
Understand how your AI actually performs in the wild
FAQs
How is this different from DeepEval?
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.
Click here for a more comprehensive comparison.
What LLM use cases are supported?
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.
What about complex agentic systems?
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.
Who uses Confident AI?
Our platform is designed for cross-functional AI teams:
- Engineers use evals in CI/CD and traces for debugging
- QAs build test datasets and run regression suites
- PMs track quality metrics and compare experiments
- SMEs & Annotators label data and review AI outputs in human-in-the-loop workflows
Is Confident AI enterprise ready?
Yes. We offer SSO, team-based data segregation, customizable user roles and permissions, and self-hosted deployment options for your cloud environment.
What about HIPAA compliance?
We’re HIPAA compliant and sign BAAs with customers on the Premium plan or above.
Can I self-host Confident AI?
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.
What is the pricing?
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.
