4 compared
LLM evaluation tools compared
Evaluation and experimentation platforms for LLM apps, compared on approach, source, and setup.
Open in the interactive comparison tool| Field | Braintrust Evaluation, prompt experimentation, logging, and data platform for production AI application development. Open dossier | Promptfoo Open-source prompt testing and red-teaming framework for LLM outputs, regressions, evaluations, and security checks. Open dossier | DeepEval Open-source Python framework for unit-testing LLM applications, agents, RAG pipelines, metrics, regression suites, and traces. Open dossier | Arize Phoenix Open-source observability and evaluation tooling for LLM applications, traces, datasets, and experiments. Open dossier |
|---|---|---|---|---|
| Trust | ||||
| Install risk | Review first | Review first | Review first | Review first |
| Notes | Safety · Privacy ✓ | Safety · Privacy ✓ | Safety ✓ Privacy ✓ | Safety · Privacy · |
| Category | tools | tools | tools | tools |
| Source | source-backed | source-backed | source-backed | source-backed |
| Author | Braintrust | Promptfoo | Confident AI | Arize AI |
| Added | 2026-04-27 | 2026-04-27 | 2026-06-03 | 2026-04-27 |
| Platforms | CLI | CLI | CLI | CLI |
| Source repo | — | — | — | — |
| Safety notes | — missing | — missing | ✓DeepEval metrics should be treated as regression and review signals, not proof that an LLM application is safe, correct, or production-ready. LLM-as-a-judge metrics can call configured model providers, consume quota, hit rate limits, and produce judge-model errors that need separate handling. Evaluation thresholds should be calibrated on real examples before they block deployments or trigger automated rollback, ranking, billing, or moderation decisions. Tracing instrumentation can wrap live application code, agents, retrievers, tools, and model calls; keep eval and production environments clearly separated. | — missing |
| Privacy notes | ✓Braintrust receives the prompts, model outputs, eval datasets, and logs you send for experimentation and scoring; review what test and production data leaves your environment before uploading sensitive content. | ✓Promptfoo sends your prompts and test inputs to the model providers you configure to run evals and red-team probes; review which providers are used and keep secrets out of test cases. | ✓Test cases, traces, spans, prompts, actual outputs, expected outputs, retrieval context, tool arguments, metadata, and evaluation results may contain sensitive user or business data. LLM-based metrics can send evaluation payloads to the configured model provider unless a reviewed local model path is used. DeepEval documentation says evaluations run locally by default, while Confident AI login and cloud reporting are optional paths for centralized results. The official data privacy docs say DeepEval collects basic PostHog telemetry by default, including event names, metric names, notebook usage, an anonymous UUID, and public IP, with `DEEPEVAL_TELEMETRY_OPT_OUT=1` available for opt-out. | — missing |
| Prerequisites | — none listed | — none listed |
| — none listed |
| Install | — | — | — | — |
| Config | — | — | — | — |
| Citations | ||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
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