What Confident AI does
Confident AI is the managed DeepEval platform for LLM regression testing, experiments, observability, prompt workflows, annotations, simulations, and red teaming.
Confident AI is the collaborative platform built around the open-source DeepEval framework. It adds shared testing reports, datasets, prompt workflows, online evaluations, trace observability, annotations, alerts, simulations, and enterprise governance to code-first tests. Local DeepEval can remain useful independently when a team needs a lighter or private workflow.
Free supports two users, one project, five weekly test runs, and limited trace storage. Starter is $200 per organization monthly with five projects and included trace storage; Team is $2,000 monthly with unlimited projects, RBAC, SSO, SOC 2 features, and more storage. Enterprise is custom. Trace retention overages, evaluator tokens, model calls, and red-teaming modules can add cost.
Many DeepEval metrics use an LLM as judge, so results depend on the configured model, rubric, input fields, and randomness. Uploading traces or datasets can expose prompts, retrieved content, personal data, and internal tools. Mask sensitive fields, separate projects, choose regions and private deployment where needed, compare judges with human labels, and avoid treating an attractive dashboard or one regression score as proof of safety.
How Confident AI works
Developers define DeepEval test cases, datasets, traces, and deterministic or model-based metrics locally, then optionally authenticate to Confident AI to sync reports and telemetry. The platform organizes projects, runs cloud or API evaluations, compares regressions, versions prompts and metrics, queues human annotations, monitors live traces, and supports simulations. Teams select the judge model and rubric, inspect evidence, and approve prompt or application changes.
How to set up Confident AI
Choose local and managed boundaries
Decide which tests run only in DeepEval, which results or traces sync, approved judge providers, regions, retention, and project access.
Create a representative test suite
Encode common tasks, known failures, adversarial inputs, multi-turn cases, tool outcomes, languages, and human-authored rubrics or expected values.
Instrument with minimized data
Add trace decorators and version metadata, mask identifiers and secrets, keep API keys scoped, and verify what the dashboard stores.
Calibrate metrics and annotations
Compare deterministic and LLM judges with expert labels, measure repeatability and slice errors, version rubrics, and adjudicate disagreements.
Add controlled release and monitoring
Run regression tests in CI, review failures, sample live traffic, alert accountable owners, require prompt approvals, and rehearse rollback and deletion.
Confident AI FAQs
Is Confident AI the same as DeepEval?
No. DeepEval is the open-source testing framework; Confident AI is its managed collaboration, observability, and governance platform.
Is there a free plan?
Yes. It limits users, projects, weekly test runs, and trace storage.
How much are paid plans?
Current monthly pricing lists Starter at $200 and Team at $2,000 per organization; usage overages and Enterprise capabilities have separate terms.
Can DeepEval run locally?
Yes. Tests and custom judge integrations can run locally; syncing results or using cloud features changes the data flow.
Are LLM-as-a-judge scores objective?
No. They reflect a chosen model and rubric. Validate them against diverse expert decisions and inspect individual errors.
Listing reviewed 2026-07-31. Product details and pricing can change; verify important terms on the provider's website.
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