LLM observability & evaluation · Confident AI

DeepEval

Open-source Python evaluation framework with 30+ research-backed metrics, with an optional paid platform (Confident AI) for CI/CD and hosted reporting.

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DeepEval is an open-source Python library, similar in spirit to a unit-testing framework, for evaluating LLM outputs with more than thirty single-turn and fifteen multi-turn research-backed metrics (covering faithfulness, relevance, bias, and other quality dimensions), plus support for custom metrics and integration into standard test runners so evaluations run in CI/CD. As a library it is free with no usage limits and does not itself host results. Confident AI is the company's separate hosted platform layered on top of DeepEval, adding no-code evaluation workflows, online (production) evaluation, annotation queues, metric versioning, and team collaboration, priced as a monthly subscription with a small free tier and usage-based overage for LLM tokens and stored trace spans beyond each plan's allowance.

At a glance

Vendor Confident AI
Pricing model Free tier + paid plans
Free tier Yes
Deployment Cloud, Self-hosted
Open source Yes (Apache-2.0)
Best for Engineering teams that want LLM evaluation as unit-test-style code in CI/CD, with an optional hosted layer for production evals.

Pricing

DeepEval the library is free and open source with no limits; the companion Confident AI platform has a free tier plus Starter, Team, and Enterprise monthly subscriptions with token/trace-span overage.

Plan Price Notes
DeepEval (library) Free Open source, self-hosted, no usage limits
Confident AI Free $0/month 2 user seats, 1 project, 5 test runs/week
Confident AI Starter $200/month No-code workflows, custom metrics, online evals, annotation queues, up to 5 projects
Confident AI Team $2,000/month Metric versioning, Git-based prompt workflows, custom RBAC, SOC 2, SSO, unlimited projects

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • 30+ single-turn and 15+ multi-turn research-backed evaluation metrics
  • Open-source Python library, testable in standard CI/CD pipelines
  • Custom metric support
  • Online (production) evaluation on the Confident AI platform
  • Annotation queues and metric versioning
  • Git-based prompt workflows on Team/Enterprise

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Ragas Open-source Python framework of reference-free metrics purpose-built for evaluating retrieval-augmented generation (RAG) pipelines. Braintrust Evaluation-first platform for testing, scoring, and comparing LLM application changes, with production tracing built in. Patronus AI Evaluation-focused platform offering pre-built and custom evaluator models to score LLM outputs for accuracy, safety, and quality.

DeepEval in the index now

Terms to know

Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. LLM evaluation Systematically testing a language model's outputs for quality, accuracy, safety, and consistency before and after deployment. LLM observability Monitoring a language model's live behavior in production, tracking its inputs, outputs, latency, cost, and failure patterns.

Related guides

How to choose an LLM observability tool Pick an LLM tool by whether you need tracing, scoring or a gateway first — most teams eventually need all three, rarely from one vendor. How to evaluate LLM applications Offline evals, LLM-as-judge, human review and production monitoring, aimed at the whole pipeline rather than the model alone.