Comparison
Braintrust vs LLMJury
Braintrust is built around the eval. LLMJury is built around the online experiment — the same question, asked of real traffic.
Braintrust is evaluation and dataset tooling for AI products, with a playground and scoring functions. Below is what it does well, where LLMJury does something different, and why the two are usually complements rather than a choice.
What Braintrust is good at
- Eval ergonomics. Writing a scorer, running it across a dataset, and comparing two runs side by side is fast and pleasant, which is most of what offline evaluation needs.
- Dataset management as a first-class object — versioned, shareable, and easy to grow from production examples.
- A playground for iterating on prompts against real examples with immediate scored feedback.
- Good support for custom scoring functions, including code-based scorers alongside model-graded ones.
Where LLMJury does something else
An eval is a measurement on a set you chose. An experiment is a measurement on the users you have. The gap between them is where regressions live: distribution drift, the long tail your dataset does not contain, and the outcomes — did they convert, did they come back — that simply do not exist offline.
LLMJury runs the online half. A deterministic split so the comparison is fair, a judge sampling under a hard budget so grading cannot run away with your bill, and a verdict only once the numbers clear an SRM check and FDR correction across every metric compared.
Automatic stopping is the part that is hard to add later: when the observed traffic split diverges from the configured allocation, the analysis halts instead of reporting a result built on a broken assignment.
Side by side
| Dimension | LLMJury | Braintrust |
|---|---|---|
| Built for | LLM products: prompt and model variants, judged on live traffic | Evaluating AI products against datasets, with scoring functions and a playground |
| Core unit | The online experiment — a variant split running on real users | The eval run over a dataset |
| Quality measurement | Built-in LLM-as-judge (quality, safety, relevance) plus your own rubric metrics | Custom scorers, model-graded and code-based, over datasets |
| Live-traffic comparison | The product — variant assignment on real users | Logging and online scoring; not a controlled user split |
| Statistical treatment | Chi-squared SRM hard gate (analysis halts, no result shown), per-metric test routing, Benjamini–Hochberg FDR correction across every comparison, raw and corrected p-values both reported | Score aggregates and run-over-run comparison |
| Broken-test detection | SRM gate halts the analysis rather than reporting a result | Not applicable — no traffic split to check |
| Assignment | Deterministic local MurmurHash3 bucketing, identical in Python, TypeScript, and Java — no network call on the request path | Not applicable |
Written from each product’s own published positioning, and last checked against it on 3 August 2026. Both products move — if something here is out of date or unfair, tell us at [email protected] and we will correct it.
Should you run both?
Use Braintrust to decide which two or three candidates are worth putting in front of users, and LLMJury to decide which of them actually wins there. The offline pass narrows the field cheaply; the online pass settles it on the only population whose opinion you are paid to care about.