Comparison
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.
Arize Phoenix is an open-source LLM observability and evaluation platform built on OpenTelemetry, self-hostable or hosted. Below is what it does well, where LLMJury does something different, and why the two are usually complements rather than a choice.
What Arize Phoenix is good at
- OpenTelemetry-native tracing, which means it fits a stack that already has OTel rather than asking for a second, parallel instrumentation story. That is a genuinely good architectural decision.
- It is self-hostable and open source, so a team with a hard data-residency requirement can run the whole thing themselves — something LLMJury cannot offer today.
- The evaluation library is strong and usable independently of the platform, including for RAG-specific measures like retrieval relevance and groundedness.
- Notebook-first workflows make exploratory analysis of traces genuinely pleasant, which matters more than it sounds when you are trying to work out what went wrong.
Where LLMJury does something else
Tracing and evaluation both answer questions about outputs that already happened. What they cannot do is establish that one version caused a difference — for that you need two versions running on comparable users at the same time, which is a different mechanism, not a deeper analysis of the same data.
LLMJury is that mechanism and nothing else. Users are assigned deterministically and stickily, exposures are recorded, a sample of outputs is judged, and the comparison is between arms rather than between time periods. Comparing last month to this month in a trace store means comparing two different months of users, and every difference in those users is inside the number.
The statistical treatment is the other half. A chi-squared SRM gate halts analysis rather than reporting a result computed on a broken split, and Benjamini-Hochberg correction is applied across every metric-by-variant comparison — which matters the moment you are watching four metrics across three variants, where uncorrected p-values will hand you a winner that is not one.
Side by side
| Dimension | LLMJury | Arize Phoenix |
|---|---|---|
| Built for | LLM products: prompt and model variants, judged on live traffic | Tracing, observability, and evaluation of LLM applications |
| Core unit | The online experiment — a variant split running on real users | The trace, and the eval run over spans or a dataset |
| Quality measurement | Built-in LLM-as-judge (quality, safety, relevance) plus your own rubric metrics | An evaluation library, including RAG-specific measures |
| Live-traffic comparison | The product — real users split, exposed, and measured | Not the focus; the unit is a trace, not an arm |
| 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 aggregations over traces |
| 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 playground and versioning within the platform |
| Self-hosting | Not available today — it is on the roadmap and named as a gap | Yes — open source and self-hostable |
| Instrumentation | Deterministic local MurmurHash3 bucketing, identical in Python, TypeScript, and Java — no network call on the request path | OpenTelemetry instrumentation of your application |
Written from each product’s own published positioning, and last checked against it on 8 August 2026. Both products move — if something here is out of date or unfair, tell us at [email protected] and we will correct it.
Questions people ask on this page
- Can I use Phoenix and LLMJury together?
- Yes. Phoenix instruments your application and stores traces; LLMJury assigns users to variants locally and records exposures and outcomes. They do not share storage or instrumentation, so running both costs you one extra SDK and nothing else.
- I need to self-host. Does that rule LLMJury out?
- Today, yes — LLMJury is hosted only. The SDKs are open source and Apache-2.0, so the code closest to your application is inspectable, but the service is not something you can run yourself. Self-hosting is named as a gap on our roadmap rather than implied to exist, and Phoenix is a genuinely good answer if that is a hard requirement now.
Should you run both?
They answer adjacent questions and neither replaces the other. Phoenix tells you what happened inside a request and lets you dig; LLMJury tells you which of two versions to ship and why the number is trustworthy. If self-hosting is a requirement for you today, Phoenix can do that and LLMJury cannot — that is a real difference and it is worth saying plainly.