Comparison
Statsig vs LLMJury
Statsig is a strong general experimentation platform. LLMJury is the same discipline, purpose-built for LLM output.
Statsig is product experimentation, feature flags, and analytics. Below is what it does well, where LLMJury does something different, and why the two are usually complements rather than a choice.
What Statsig is good at
- Breadth. Feature flags, experiments, analytics, and warehouse-native deployment in one platform, across a long list of SDK languages.
- Mature experimentation statistics for product metrics — sequential testing, CUPED variance reduction, and the guardrail tooling a large organisation needs.
- It is the right tool for the experiments that are not about model output: pricing, onboarding, layout, everything with a conversion event at the end.
Where LLMJury does something else
A general platform can split traffic between two prompts. What it cannot do is tell you whether the answers got better, because the metric does not exist yet — someone has to read the outputs and score them. That is the piece LLMJury supplies: an LLM-as-judge grading a sample against your rubric, sampled and hard-budgeted, producing the quality metric the experiment is actually about.
The rest follows from that. Prompt variants and their version history are first-class objects rather than flag payloads, so the version you edit is the variant that gets tested. And the statistics are tuned for what LLM experiments actually produce — heavy-tailed latency and cost distributions, where a permutation test with bootstrap intervals is correct and a t-test quietly is not.
Side by side
| Dimension | LLMJury | Statsig |
|---|---|---|
| Built for | LLM products: prompt and model variants, judged on live traffic | General product experimentation, feature flags, and analytics |
| Output quality measurement | Built-in LLM-as-judge (quality, safety, relevance) plus your own rubric metrics | Bring your own quality signal — you supply the metric |
| 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 | Sequential testing and CUPED on product metrics |
| 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 | Config and flag values, versioned as flags |
| Assignment | Deterministic local MurmurHash3 bucketing, identical in Python, TypeScript, and Java — no network call on the request path | SDKs across many languages |
| Pricing entry | Free plan, 50k events/mo, no credit card; Pro $149/mo adds LLM-as-judge | Free tier with usage-based paid plans — check their pricing page, it moves |
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.
Questions people ask on this page
- Can I use Statsig and LLMJury together?
- Yes, and the split is usually clean: Statsig for product feature flags and general experimentation, LLMJury for the LLM surface - prompt variants as first-class objects, judge-scored quality, and statistics tuned for heavy-tailed latency and cost.
- Why not just put my prompts behind a feature flag?
- You can, and for a single on/off test it works. What it does not give you is prompt version history with a field-level diff, an LLM-as-judge scoring the outputs, or tests chosen for distributions where a t-test gives the wrong p-value. Those are the three things that make the LLM case different.
Should you run both?
If you already run Statsig, keep it. It is the better tool for every experiment whose outcome is a click. Add LLMJury for the experiments whose outcome is a paragraph of generated text, where the metric has to be produced before it can be measured.