Comparison
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.
Building it yourself is the honest default — a bucketing function in your codebase, events in your warehouse, and a notebook. Below is what it does well, where LLMJury does something different, and why the two are usually complements rather than a choice.
What Building it yourself is good at
- It is free, it is yours, and for a single experiment it is the right call. A modulo on a user id, an event per exposure, and a query at the end will answer one clear question perfectly well.
- No vendor, no data leaving your infrastructure, no procurement. For some teams that is not a preference, it is the constraint.
- You already have most of the pieces. Event pipelines, a warehouse, and someone who remembers their statistics — a first experiment is a couple of days of work, not a project.
- And you learn what the machinery actually does, which makes you a better judge of any tool you buy later, including this one.
Where LLMJury does something else
The second experiment is where the cost shows up, and it is not the assignment function. It is everything that makes the answer trustworthy: a bucketing hash that gives the same user the same variant in all three of your languages and does not reshuffle when someone edits the config; a sample-ratio-mismatch check that halts the analysis instead of reporting a number built on a broken split; false-discovery-rate correction once you are looking at four metrics rather than one; and tests that suit the distributions LLM work produces — p95 latency and token cost are heavy-tailed, and a t-test on them is quietly optimistic.
Then the LLM-specific half, which has no equivalent in a normal A/B stack: a judge that scores outputs against a rubric, sampled and hard-budgeted so grading does not outspend the thing it is grading, with its verdicts cached and its rubric versioned — because a rubric that changes mid-experiment invalidates the comparison as surely as a broken split does.
And prompt versioning, which is the part teams reliably skip and then need at the worst moment: history, authorship, a diff, and a rollback that does not go through a deploy. Each of these is a week you could spend. The question is not whether you could build it; it is whether this is the thing you want to maintain while the product it is measuring keeps changing.
Side by side
| Dimension | LLMJury | Building it yourself |
|---|---|---|
| Built for | LLM products: prompt and model variants, judged on live traffic | Whatever you build it for — usually one question, well |
| Core unit | The online experiment — a variant split running on real users | A query over your own events |
| Quality measurement | Built-in LLM-as-judge (quality, safety, relevance) plus your own rubric metrics | You write the judge, the rubric, the sampling, and the budget cap |
| 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 | Whatever you implement — SRM and FDR are the two most often skipped |
| Assignment | Deterministic local MurmurHash3 bucketing, identical in Python, TypeScript, and Java — no network call on the request path | Straightforward in one language; the work is keeping three in agreement |
| 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 | String literals and git, unless you build the store too |
| Cost | Free plan, 50k events/mo, no credit card; Pro $149/mo adds LLM-as-judge | Engineering time, then maintenance, then the experiment nobody trusts |
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?
If you are running one experiment to settle one argument, build it — seriously, it is a good use of two days and you will understand the problem better afterwards. Come back when experiments become a habit rather than an event, or the first time a result turns out to have been built on a broken split and nothing told you.