How do you measure judge-model token usage and cost for a Ragas evaluation run?

## Why this is a real question and not trivia Every LLM-based metric in Ragas is a judge model call. That is easy to forget while iterating on ten samples, and expensive to remember at three thousand. Teams routinely discover that the evaluation of a change costs more than serving the feature did for the same period, because evaluation runs at full metric fan-out over every row while production serves one call per user request. ## Turning measurement on Usage tracking is opt-in. `evaluate()` accepts `token_usage_parser`, a callable that knows how to read token counts out of whatever LLM response object your evaluator produces. Ragas ships `get_token_usage_for_openai` in `ragas.cost` for OpenAI-shaped responses. Attach it and the run accumulates usage as it goes. With the parser attached, the returned `EvaluationResult` answers two questions: - `result.total_tokens()` — the run's aggregate token usage, split into input and output. - `result.total_cost(cost_per_input_token=..., cost_per_output_token=...)` — that usage multiplied by the per-token rates you pass in. Ragas deliberately does not embed a price table, because provider pricing changes and would go stale in a pinned library version. You supply the rates, which also means you can supply your own negotiated or internal chargeback rates. Without the parser, the result has no usage to report. There is no retroactive accounting — you cannot ask a finished run what it cost if you did not measure while it ran. ## Where the cost actually comes from The naive model is samples × metrics. The real model has three more multipliers: **Calls per metric.** Several metrics decompose the work. A faithfulness-style metric typically extracts claims from the answer in one call, then verifies claims against the context in further calls. One "metric score" can be several judge requests. **Input size.** RAG evaluation prompts carry the retrieved contexts. Contexts are the biggest part of the payload, so a pipeline that retrieves ten chunks costs roughly twice as much to evaluate as one retrieving five, for identical sample counts. Input tokens usually dominate output tokens in this workload by a wide margin. **Retries.** Every retried call is billed. A run pushed hard against a throttling provider can bill a multiple of its nominal cost for the same set of scores. ## The estimation ritual Before any large run: take a random fifty samples from the dataset, run the exact metric set with the parser attached, read `total_tokens()` and `total_cost(...)`, divide by fifty, and multiply by the real dataset size. Do this again whenever the metric set, the judge model, the retrieval depth, or the prompt changes — any of the four moves the per-sample cost. The estimate also tells you which lever to pull if the number is unacceptable. If input tokens dominate, the lever is context size or dataset size, not judge choice. If one metric accounts for most of the calls, the lever is dropping or downgrading that metric. If retries are inflating the total, the lever is concurrency. ## The levers, in rough order of leverage - **Fewer samples.** A well-chosen, stratified few hundred usually tells you what a few thousand does, at a tenth the cost. - **Fewer metrics per run.** Score the two metrics that gate your decision on every run; keep the diagnostic long tail for occasional deep dives. - **A cheaper judge** — but verify it first, because a weaker judge produces more unparseable outputs and therefore more unscored rows, and its scores may not be comparable to your existing history. - **Caching**, so an iteration that re-scores an unchanged dataset does not re-pay for identical judge calls. - **Lower concurrency**, to stop paying for retried requests. ## Reporting it A mature setup records tokens and cost as run metadata beside the scores. That single number turns "we should evaluate more often" from an opinion into a budget line, and it is what lets you argue for a nightly full run versus a per-change subset with actual figures rather than intuition.