DeepEval vs Inspect AI
A side-by-side comparison of DeepEval and Inspect AI, two Eval tools, drawn from Ignaite's continuously-verified listings.
Compared from listings verified as of
At a glance
| Attribute | DeepEval | Inspect AI |
|---|---|---|
| Category | Eval | Eval |
| Pricing (differs) | FREEMIUM | FREE |
| License (differs) | Open core | Open source |
| Deployment (differs) | Hybrid | — |
| Platforms | CLI, API | CLI, API |
| Model support | BYO key / model | BYO key / model |
| Vendor (differs) | Confident AI | UK AI Security Institute |
The honest brief
DeepEval
Write LLM evals as Pytest-style assertions and run them in CI, backed by 50+ metrics across RAG, agents, and safety.
- Assertions run in your CI pipeline
- Metrics for RAG, agents, and safety
- Bring any judge model (BYO key)
- Integrates LangChain/CrewAI/OpenAI
- LLM-as-judge adds cost
- Dashboards need paid Confident AI
- Judge metrics can be noisy
Inspect AI
Built by the UK AI Security Institute and adopted by Anthropic, DeepMind, METR, and Apollo as a shared eval framework; MIT.
- Adopted across major safety labs
- Composable datasets/solvers/scorers
- 200+ prebuilt evals (inspect_evals)
- Sandboxed tool + multi-turn agent runs
- MIT-licensed, provider-agnostic
- Python/code framework, not a UI product
- Steeper than no-code eval tools
- You wire up your own model keys