Comparison
Langfuse vs LLMJury
Langfuse traces what your LLM app did. LLMJury decides which version of it is better, on live traffic.
Langfuse is open-source LLM observability and tracing, with evals and a prompt store. Below is what it does well, where LLMJury does something different, and why the two are usually complements rather than a choice.
What Langfuse is good at
- Tracing depth. Nested spans across chains, agents, and retrieval steps, with token and cost attribution per span — if you need to understand one request end to end, this is the tool.
- Open source and self-hostable, which matters when your data cannot leave your infrastructure.
- A large integration surface across the Python and JS ecosystems, so instrumenting an existing app is usually quick.
- Dataset-based evals and annotation queues for offline scoring during development.
Where LLMJury does something else
Tracing tells you what happened. It does not tell you whether prompt B is better than prompt A, because answering that is not an observability question — it is an experiment: a split of real users, a controlled comparison, and a statistical test that separates a real difference from noise.
LLMJury is that layer. It assigns each user deterministically to a variant, grades a sample of outputs against your rubric, and reports a verdict that has cleared a sample-ratio-mismatch check and false-discovery-rate correction. When the split breaks, it stops and says so rather than showing you a number that is not true.
The prompt versioning is wired into the same loop rather than sitting beside it: the version you edit is the variant that gets tested and the one the verdict is about.
Side by side
| Dimension | LLMJury | Langfuse |
|---|---|---|
| Built for | LLM products: prompt and model variants, judged on live traffic | Observability: tracing, debugging, and offline evaluation of LLM applications |
| Core unit | The online experiment — a variant split running on real users | The trace — one request, fully instrumented |
| Quality measurement | Built-in LLM-as-judge (quality, safety, relevance) plus your own rubric metrics | Evals over datasets, plus human annotation queues |
| Live-traffic comparison | The product — variant assignment, exposure tracking, and a verdict per metric | Not the focus; traces are recorded, not split and compared |
| 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 | Eval scores and aggregates |
| Prompt management | Prompts live outside your repo with version history, audit trail, diff, and one-click rollback — and the version you edit is the variant that gets tested | Prompt management with versioning and labels |
| Hosting | Hosted; SDKs are open source | Open source, self-hostable or cloud |
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?
Most teams that run experiments still want traces. Keep Langfuse for understanding a single request and debugging what your chain actually did; add LLMJury when the question changes from “what happened?” to “which version should we ship?”. They instrument different moments and do not conflict.