What Braintrust does well: eval-first workflows — datasets, scoring functions, experiments comparing prompt/model variants side by side, and a fast feedback loop for iterating on prompts before you ship. If your main question is "which of these two prompts scores better on my eval set," Braintrust is built exactly for that.
What Dunetrace adds: Braintrust's strength is structured, offline-style evaluation against datasets you define — it isn't designed to watch every live production run and intervene mid-execution. Dunetrace's structural detectors run in-path on every completed run automatically, with zero eval-set setup required, and its runtime prevention policies can stop or redirect a run while it's still executing — something an eval framework, by design, doesn't do.
Side by side
| Dunetrace | Braintrust | |
|---|---|---|
| Primary use case | Real-time production monitoring + prevention | Prompt/model experimentation and eval during development |
| Can stop a run mid-flight | Yes — runtime prevention policies | No — eval and experimentation tooling, not in-path |
| Structural failure detection | 23 detectors, zero LLM calls, automatic, no setup | Not built for this — scoring functions run against logged data |
| Dataset-based experiments | No | Yes — this is a core strength |
| Production alerting | Slack / webhook / Linear, <15s | Not the primary design center |
| LLM-based evaluation of live runs | Built in (semantic evaluation), sampling-based, or pull Braintrust's own scores in | Yes, via logged spans and scoring functions |
Use both
Dunetrace can pull Braintrust's evaluation results in directly, correlated to the same runs via trace_id. Use Braintrust to iterate on prompts and models before shipping; let Dunetrace watch what actually happens in production and enforce guardrails an offline eval can't. See the Braintrust integration docs.