LLMJury compared to the tools next to it

Most of these are not competitors so much as neighbours: they solve the parts of the problem either side of the one LLMJury solves. Each page opens with what the other product is genuinely better at, because a comparison that skips that part is not worth reading — and that includes the last one, which is building it yourself.

Langfuse vs LLMJury
Langfuse traces what your LLM app did. LLMJury decides which version of it is better, on live traffic.
LangSmith vs LLMJury
LangSmith is where you develop and debug an LLM app. LLMJury is where you prove a change to it was an improvement.
Braintrust vs LLMJury
Braintrust is built around the eval. LLMJury is built around the online experiment — the same question, asked of real traffic.
Statsig vs LLMJury
Statsig is a strong general experimentation platform. LLMJury is the same discipline, purpose-built for LLM output.
promptfoo vs LLMJury
promptfoo is where you test a prompt before it ships. LLMJury is where you find out what it did after.
Helicone vs LLMJury
Helicone sits in front of your model calls and records them. LLMJury sits around them and compares two versions of what you send.
OpenAI Evals vs LLMJury
OpenAI Evals scores a model against a dataset you assembled. LLMJury scores a change against the traffic your users actually send.
Arize Phoenix vs LLMJury
Phoenix is open-source observability and evaluation for LLM applications. LLMJury is the controlled online experiment that decides between two versions.
W&B Weave vs LLMJury
Weave brings LLM tracking into the machine-learning platform your team may already run. LLMJury runs the online experiment that decides what ships.
Building it yourself vs LLMJury
For one experiment, a hash function and a spreadsheet genuinely are enough. This page is about what changes when it is not one experiment.

All of them, at a glance

6 questions, 10 tools. Derived rather than counted by hand — this line was written when there were seven, and was wrong by the time the eval platforms were added. The nuance is on the pages above; this is the shape of the category.

LLMJury and seven alternatives across six dimensions
Dimension	LLMJury	Langfuse	LangSmith	Braintrust	Statsig	promptfoo	Helicone	OpenAI Evals	Arize Phoenix	W&B Weave	Building it yourself
Primary job	Online experiments on LLM output	Tracing and debugging requests	Developing and debugging LLM apps	Offline evals over datasets	General product experimentation	Testing prompts locally and in CI	Gateway logging, caching, cost control	Offline evaluation against a dataset	LLM observability and evaluation	LLM tracking inside an ML platform	Whatever you build it for
Splits live traffic	Yes — deterministic and sticky	No	No	No	Yes — it is the core of the product	No — your test cases, not users	No	No	No	No	Yes, once you write the hash
Scores output quality	LLM-as-judge, plus your own rubric	Dataset evals and human annotation	Dataset evals, judges, human review	Custom scorers, model- and code-based	You supply the quality metric	Assertions and model-graded rubrics	Scoring and feedback on logged requests	Yes — graders over your dataset	Yes — its own eval library	Yes — scorers over runs	Yours to build, sample, and budget
Statistical verdict	SRM-gated, FDR-corrected	Eval scores and aggregates	Scores per dataset run	Score aggregates, run over run	Sequential testing and CUPED	Pass or fail, per test case	Usage and cost aggregates	Scores, not significance	Aggregations, not significance	Run comparison, not significance	Whatever you implement
Prompt versioning	Yes — the version tested is the one shipped	Yes, with labels	Prompt hub with versions	Playground with versions	Flag values, versioned as flags	Files in your repo, versioned by git	Prompt store with versions	Depends on your setup	Yes — in-platform playground	Yes — versioned objects	String literals and git
Sits on your request path	No — assignment is a local hash	No — instrumented, not proxied	No — instrumented, not proxied	No — instrumented, not proxied	No — SDK, no added call	No — it runs before you deploy	Yes — it is a proxy	No — runs offline	No — OTel instrumentation	No — tracking decorators	Your call

Langfuse

Primary job: Tracing and debugging requests; LLMJury: Online experiments on LLM output
Splits live traffic: No; LLMJury: Yes — deterministic and sticky
Scores output quality: Dataset evals and human annotation; LLMJury: LLM-as-judge, plus your own rubric
Statistical verdict: Eval scores and aggregates; LLMJury: SRM-gated, FDR-corrected
Prompt versioning: Yes, with labels; LLMJury: Yes — the version tested is the one shipped
Sits on your request path: No — instrumented, not proxied; LLMJury: No — assignment is a local hash

LangSmith

Primary job: Developing and debugging LLM apps; LLMJury: Online experiments on LLM output
Splits live traffic: No; LLMJury: Yes — deterministic and sticky
Scores output quality: Dataset evals, judges, human review; LLMJury: LLM-as-judge, plus your own rubric
Statistical verdict: Scores per dataset run; LLMJury: SRM-gated, FDR-corrected
Prompt versioning: Prompt hub with versions; LLMJury: Yes — the version tested is the one shipped
Sits on your request path: No — instrumented, not proxied; LLMJury: No — assignment is a local hash

Braintrust

Primary job: Offline evals over datasets; LLMJury: Online experiments on LLM output
Splits live traffic: No; LLMJury: Yes — deterministic and sticky
Scores output quality: Custom scorers, model- and code-based; LLMJury: LLM-as-judge, plus your own rubric
Statistical verdict: Score aggregates, run over run; LLMJury: SRM-gated, FDR-corrected
Prompt versioning: Playground with versions; LLMJury: Yes — the version tested is the one shipped
Sits on your request path: No — instrumented, not proxied; LLMJury: No — assignment is a local hash

Statsig

Primary job: General product experimentation; LLMJury: Online experiments on LLM output
Splits live traffic: Yes — it is the core of the product; LLMJury: Yes — deterministic and sticky
Scores output quality: You supply the quality metric; LLMJury: LLM-as-judge, plus your own rubric
Statistical verdict: Sequential testing and CUPED; LLMJury: SRM-gated, FDR-corrected
Prompt versioning: Flag values, versioned as flags; LLMJury: Yes — the version tested is the one shipped
Sits on your request path: No — SDK, no added call; LLMJury: No — assignment is a local hash

promptfoo

Primary job: Testing prompts locally and in CI; LLMJury: Online experiments on LLM output
Splits live traffic: No — your test cases, not users; LLMJury: Yes — deterministic and sticky
Scores output quality: Assertions and model-graded rubrics; LLMJury: LLM-as-judge, plus your own rubric
Statistical verdict: Pass or fail, per test case; LLMJury: SRM-gated, FDR-corrected
Prompt versioning: Files in your repo, versioned by git; LLMJury: Yes — the version tested is the one shipped
Sits on your request path: No — it runs before you deploy; LLMJury: No — assignment is a local hash

Helicone

Primary job: Gateway logging, caching, cost control; LLMJury: Online experiments on LLM output
Splits live traffic: No; LLMJury: Yes — deterministic and sticky
Scores output quality: Scoring and feedback on logged requests; LLMJury: LLM-as-judge, plus your own rubric
Statistical verdict: Usage and cost aggregates; LLMJury: SRM-gated, FDR-corrected
Prompt versioning: Prompt store with versions; LLMJury: Yes — the version tested is the one shipped
Sits on your request path: Yes — it is a proxy; LLMJury: No — assignment is a local hash

OpenAI Evals

Primary job: Offline evaluation against a dataset; LLMJury: Online experiments on LLM output
Splits live traffic: No; LLMJury: Yes — deterministic and sticky
Scores output quality: Yes — graders over your dataset; LLMJury: LLM-as-judge, plus your own rubric
Statistical verdict: Scores, not significance; LLMJury: SRM-gated, FDR-corrected
Prompt versioning: Depends on your setup; LLMJury: Yes — the version tested is the one shipped
Sits on your request path: No — runs offline; LLMJury: No — assignment is a local hash

Arize Phoenix

Primary job: LLM observability and evaluation; LLMJury: Online experiments on LLM output
Splits live traffic: No; LLMJury: Yes — deterministic and sticky
Scores output quality: Yes — its own eval library; LLMJury: LLM-as-judge, plus your own rubric
Statistical verdict: Aggregations, not significance; LLMJury: SRM-gated, FDR-corrected
Prompt versioning: Yes — in-platform playground; LLMJury: Yes — the version tested is the one shipped
Sits on your request path: No — OTel instrumentation; LLMJury: No — assignment is a local hash

W&B Weave

Primary job: LLM tracking inside an ML platform; LLMJury: Online experiments on LLM output
Splits live traffic: No; LLMJury: Yes — deterministic and sticky
Scores output quality: Yes — scorers over runs; LLMJury: LLM-as-judge, plus your own rubric
Statistical verdict: Run comparison, not significance; LLMJury: SRM-gated, FDR-corrected
Prompt versioning: Yes — versioned objects; LLMJury: Yes — the version tested is the one shipped
Sits on your request path: No — tracking decorators; LLMJury: No — assignment is a local hash

Building it yourself

Primary job: Whatever you build it for; LLMJury: Online experiments on LLM output
Splits live traffic: Yes, once you write the hash; LLMJury: Yes — deterministic and sticky
Scores output quality: Yours to build, sample, and budget; LLMJury: LLM-as-judge, plus your own rubric
Statistical verdict: Whatever you implement; LLMJury: SRM-gated, FDR-corrected
Prompt versioning: String literals and git; LLMJury: Yes — the version tested is the one shipped
Sits on your request path: Your call; LLMJury: No — assignment is a local hash

Comparing against something not listed? hello@llmjury.com — we will write it up honestly, including the parts where they win.

LLMJury compared to the tools next to it

Langfuse vs LLMJury

LangSmith vs LLMJury

Braintrust vs LLMJury

Statsig vs LLMJury

promptfoo vs LLMJury

Helicone vs LLMJury

OpenAI Evals vs LLMJury

Arize Phoenix vs LLMJury

W&B Weave vs LLMJury

Building it yourself vs LLMJury

All of them, at a glance