DeepEval is the open-source LLM evaluation framework associated with Confident AI, providing metrics and testing tools for AI applications.

Who should use Confident AI?

It is best for developers and AI product teams that need repeatable evaluations, production monitoring, red teaming, and governance.

Does Confident AI support CI/CD?

Yes. Teams can integrate LLM tests and evaluations into development and CI/CD workflows to catch regressions before release.

How is Confident AI priced?

Confident AI offers a free plan, a paid Starter tier, and higher Team or Enterprise options with pricing depending on requirements.

What should teams evaluate?

Check evaluation limits, tracing, red-team coverage, collaboration, retention, security, and enterprise requirements before choosing a plan.

Confident AI

Name: Confident AI
Brand: Confident AI

LLM evaluation, observability, red teaming, and governance platform for AI apps.

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What is Confident AI?

TL;DR

Confident AI is an LLM evaluation, observability, red teaming, and governance platform connected to DeepEval, helping teams test prompts, monitor production behavior, and keep AI regressions out of releases.

Run LLM unit tests and regression tests
Trace and monitor production LLM systems
Use DeepEval metrics, datasets, and prompt versioning

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About

Confident AI is an LLM evaluation, observability, red teaming, and governance platform for teams building AI applications. It is closely connected to DeepEval, the open-source LLM evaluation framework, and helps teams test prompts, evaluate outputs, trace production behavior, and keep regressions out of releases. The platform is useful for founders and SaaS teams that need higher confidence in AI features before shipping to users. It supports evaluation workflows, CI/CD testing, prompt versioning, cloud datasets, custom metrics, live traffic evals, human annotation, chat simulations, alerting, and downstream observability workflows. Confident AI is especially relevant for teams moving from one-off prompt experiments to production quality control. It gives product and engineering teams a shared way to define good behavior, test AI workflows, monitor failures, and improve model outputs with evidence. For FutureStack, Confident AI belongs in Developer Tools because evaluation and observability are now core infrastructure for AI products. It is best for developers, founders, and AI product teams building chatbots, agents, RAG systems, assistants, and automated workflows that need reliable behavior.

Use Cases

LLM Regression Testing

Run repeatable evaluation suites in CI/CD to detect quality regressions before model or prompt changes reach production.

Production AI Monitoring

Trace live AI workflows, monitor quality signals, and alert teams when production behavior changes.

AI Red Teaming

Test agents and LLM applications against adversarial scenarios, vulnerabilities, and unsafe behavior patterns.

AI Governance

Standardize evaluation criteria, review workflows, and quality controls across teams shipping AI products.

Key Features

Run LLM unit tests and regression tests
Trace and monitor production LLM systems
Use DeepEval metrics, datasets, and prompt versioning
Add red teaming, governance, alerts, and human review

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