# LLM evaluation tools compared for health AI teams

> Promptfoo, DeepEval, Inspect, Langfuse, Arize Phoenix and Braintrust: licences, strengths and which fits a regulated health product.

Comparison · Regions: Global · By Harsh Vitra (BrandLabs) · Published 2026-11-14 · Reviewed 2026-11-14 · Source: https://www.brandlabs.app/learn/llm-evaluation-tools-compared

**TL;DR:** Open-source eval frameworks such as Promptfoo (MIT), DeepEval (Apache-2.0) and Inspect (MIT, from the UK AI Security Institute) let you run tests in CI and keep data on your own machines. Observability platforms such as Langfuse add tracing and production scoring. For health data, self-hostable tools make privacy reviews simpler.

## Key takeaways

- Promptfoo and Inspect are MIT-licensed; DeepEval is Apache-2.0.
- Inspect was built by the UK AI Security Institute for research-grade evaluations.
- Langfuse is mostly MIT and self-hostable; some enterprise admin features need a licence key.
- Arize Phoenix is source-available under the Elastic License 2.0, not an OSI-approved open-source licence.
- The tool matters less than the test set: health scenarios, languages and rubrics written with clinicians.

## Which LLM evaluation tools do teams use?

| Tool | Licence | Best at | Self-host |
| --- | --- | --- | --- |
| Promptfoo | MIT | CLI and CI tests, red-teaming | Yes |
| DeepEval | Apache-2.0 | Python test framework with ready-made metrics | Yes |
| Inspect | MIT | Research-grade evaluations, agents | Yes |
| Langfuse | Mostly MIT | Tracing, prompt management, production scoring | Yes |
| Arize Phoenix | Elastic License 2.0 | Tracing and evaluation in notebooks and production | Yes |
| Braintrust | Closed platform | Hosted experiments and review workflows | No |

## What does a health AI eval setup need?

Three layers. **Functional** tests check that features work. **Quality** tests score answers against rubrics. **Safety** tests probe crisis handling, professional claims, medical accuracy, minors and data leakage, in every language you support. Each layer runs in CI and blocks a release when a safety score drops.

## Code-first framework or platform?

Code-first frameworks keep tests next to your code and data on your machines, which suits regulated teams. Platforms add dashboards, human review queues and production traces. Many teams use both: a framework in CI and an observability tool in production.

## What matters more than the tool?

The test set. Write scenarios from real user questions, add adversarial cases, get clinicians to define what a safe answer looks like, run each scenario several times, and keep failing cases as regression tests forever.

## Frequently asked questions

### Which eval tool should a small health startup start with?

A code-first framework you can run in CI, such as Promptfoo or DeepEval, plus a spreadsheet-sized test set written with a clinician. Add tracing and production monitoring once real users arrive.

### Can we use hosted eval platforms with patient data?

Only if the vendor's contracts, hosting region and retention meet your obligations, such as a business associate agreement under HIPAA. Many teams de-identify test data or self-host instead.

## Where BrandLabs fits

Precision, our evaluation product, scores health AI on safety and quality, and we wire evals into CI so unsafe releases are blocked. https://www.brandlabs.app/our-lab/precision

## Sources

- promptfoo, GitHub: https://github.com/promptfoo/promptfoo
- DeepEval, Confident AI on GitHub: https://github.com/confident-ai/deepeval
- Inspect AI, UK AI Security Institute: https://inspect.aisi.org.uk
- Langfuse, GitHub: https://github.com/langfuse/langfuse
- Phoenix, Arize AI on GitHub: https://github.com/Arize-ai/phoenix
- LLM and agent evaluation platforms: 2026 comparison, Arize AI, 2026: https://arize.com/resources/llm-and-agent-evaluation-platforms/