Confident AI

What is Confident AI?

‍Confident AI is the cloud platform behind DeepEval, built for hosted LLM evaluation, dataset management, and regression testing. It gives teams a place to run evaluations, review results, and track AI quality over time. (confident-ai.com)

Understanding Confident AI

‍In practice, Confident AI sits on top of DeepEval and adds the collaboration layer that many teams need once evaluation moves beyond local scripts. The platform is designed for development workflows, where teams compare prompts, models, and parameters, and for production workflows, where traces, monitoring, and alerts help catch regressions after release. (confident-ai.com)

‍It is also positioned as a shared workspace for engineers, QA, PMs, and domain experts. Confident AI documentation describes features such as dataset creation, annotation, A/B regression testing, and automated evals in CI/CD, which makes it fit naturally into an LLM stack alongside your app code, test runners, and deployment pipeline. (confident-ai.com)

‍Key features of Confident AI include:

1. Hosted evaluation workflows: Run LLM evals in the cloud instead of relying only on local scripts.
2. Dataset management: Create, annotate, version, and reuse evaluation datasets.
3. Regression testing: Compare prompt or model changes and catch breaking behavior before users do.
4. Tracing and observability: Inspect production executions with context, inputs, outputs, and metrics.
5. Team collaboration: Let non-technical reviewers participate in annotation and review workflows. (confident-ai.com)

Common use cases

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1. Prompt iteration: Compare multiple prompt versions and keep the best-performing one.
2. Dataset curation: Build golden sets from real or synthetic examples for repeatable evals.
3. CI/CD gating: Block deployments when quality metrics fall below a threshold.
4. Production monitoring: Watch live traffic for quality drift and regression signals.
5. Cross-functional review: Share evaluation results with teammates who are not writing code every day. (confident-ai.com)

Things to consider when choosing Confident AI

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1. Workflow fit: Check whether you want a platform-first experience or a library-first workflow.
2. Hosting preference: Confirm whether cloud hosting, self-hosting, or a hybrid setup matches your policies.
3. Team adoption: Make sure the UI, review flow, and permissions model work for both technical and non-technical users.
4. Evaluation depth: Review the metric coverage and whether it matches your use case, such as RAG, agents, or safety testing.
5. Integration surface: Verify how it connects with your CI/CD system, tracing stack, and existing prompt workflow. (deepeval.com)

Example of Confident AI in a stack

‍Scenario: a team ships a customer support assistant powered by an LLM, a retrieval layer, and a few prompt templates.

‍The engineers use DeepEval locally to prototype metrics, then push test runs and datasets into Confident AI for shared review. Product and QA use the hosted UI to annotate goldens, compare prompt variants, and confirm that a new model version does not regress on answer quality or safety. (deepeval.com)

‍After launch, production traces flow back into the platform so the team can spot failures, inspect examples, and turn real traffic into new evaluation cases. That makes Confident AI useful both as a testing hub and as a feedback loop for ongoing model improvement. (confident-ai.com)

PromptLayer as an alternative to Confident AI

‍PromptLayer also focuses on prompt and LLM workflow management, with visibility into prompt usage, evaluation, and iteration. If your team wants a platform centered on prompt governance and engineering workflow control, PromptLayer offers a strong alternative posture while still supporting the broader quality loop around testing and observability.

Ready to try it yourself? Sign up for PromptLayer and start managing your prompts in minutes.