Inspect AI vs DeepEval
Both are alternatives to Braintrust. Here's how they stack up — verified facts, no spin.
Also searched as DeepEval vs Inspect AI — same comparison, one verdict.
Inspect AI
The rigorous one, from the UK AI Safety Institute.
Inspect is the evaluation framework built by the UK AI Safety Institute for evaluating frontier models, released MIT. It is the most methodologically serious option here: first-class support for multi-turn agent evaluations, tool use, sandboxed execution and human grading, with a design that takes statistical validity seriously rather than producing a number that feels reassuring. If your evaluations need to withstand scrutiny — regulatory, academic or internal — this is the one.
DeepEval
Evals as pytest tests, with the research metrics already implemented.
DeepEval brings LLM evaluation into pytest, so an eval is a test function and your existing test runner, CI integration and reporting all work unchanged. It ships implementations of the metrics people actually cite — answer relevancy, faithfulness, contextual precision and recall, hallucination, bias, toxicity — including several LLM-as-judge metrics done carefully. Apache-2.0, from Confident AI, who sell an optional hosted platform.
Side by side
| Inspect AI | DeepEval | |
|---|---|---|
| Sovereignty Score | 93 | 92 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | Apache-2.0 |
| Pricing | Free, MIT, publicly funded. | Free and Apache-2.0; optional paid cloud dashboard. |
Inspect AI edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
Inspect AI
Strengths
- +Built for evaluations that have to survive real scrutiny
- +Strong agent, tool-use and sandboxed-execution support
- +Excellent log viewer for inspecting individual samples
- +MIT, from a public institute with no commercial upsell
Trade-offs
- −Aimed at model evaluation more than application regression testing
- −Steeper learning curve than Promptfoo for simple cases
- −Less oriented toward CI gating out of the box
DeepEval
Strengths
- +Pytest-native — your existing CI and reporting just work
- +Large library of implemented, research-backed metrics
- +Synthetic test-case generation for cold-start coverage
- +Apache-2.0 with no seat cost
Trade-offs
- −LLM-as-judge metrics cost tokens on every run
- −Assumes a Python codebase
- −Best dashboard experience is the paid hosted one
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Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.