The Hidden Cost of Running Your Own Evaluation Suite

An eval suite of 200 golden cases, scored by an LLM-as-judge, run on every PR — reasonable-sounding on its own, and easy to underestimate as a recurring cost. Run it on every PR, in a team merging a dozen PRs a day, and that’s 2,400+ extra LLM calls a day just for evaluation, on top of whatever the system itself costs to run.

Where the Cost Actually Comes From

flowchart TD
    A[Eval cost drivers] --> B[Number of golden cases]
    A --> C[Frequency: every PR vs every merge vs nightly]
    A --> D[Judge model choice: frontier model vs smaller model]
    A --> E[Multi-judge ensembling, if used]

The combination of a large case count, a frontier judge model (used because it’s the most reliable judge, which is a legitimate reason), and running on every PR rather than a less frequent cadence compounds fast — each factor alone seems reasonable, and multiplied together they produce a number that surprises people the first time they actually total it up.

Levers That Reduce Cost Without Reducing Coverage

Tiered eval frequency. Not every case needs to run on every PR. A fast subset (deterministic checks, no LLM-as-judge calls needed) runs on every PR; the full suite with LLM-as-judge scoring runs on merge to main, or nightly:

EVAL_TIERS = {
    "fast": {"cases": deterministic_cases, "frequency": "every_pr"},       # no LLM calls
    "full": {"cases": all_golden_cases, "frequency": "on_merge"},          # LLM-as-judge included
    "extended": {"cases": all_golden_cases + edge_cases, "frequency": "nightly"},
}

A cheaper judge model for a first pass, escalating to a stronger judge only on ambiguous cases. A smaller model can reliably distinguish a clearly-passing case from a clearly-failing one; reserve the more expensive judge specifically for cases near the decision boundary, where judgment quality actually matters most.

def two_tier_judge(response: str, criteria: str) -> dict:
    quick_score = cheap_judge.evaluate(response, criteria)
    if quick_score.confidence > 0.85:  # clearly pass or fail
        return quick_score
    return expensive_judge.evaluate(response, criteria)  # ambiguous — escalate

Deterministic checks wherever they can substitute for a judge call at all. Referenced earlier in this series — if correctness can be verified with a regex, schema validation, or exact match, that’s strictly cheaper and more reliable than an LLM-as-judge call, and it should replace one wherever the task actually allows it.

Budget for This Explicitly, Not as an Afterthought

Eval cost is genuine infrastructure spend and deserves its own line item and its own cost-attribution tracking, the same way any other recurring LLM cost does. Treating it as a rounding error until the first surprising invoice is the actual hidden cost — not the spend itself, but the fact that it wasn’t planned for.

Key Takeaways

1. Eval suite cost compounds from case count × judge model cost × run frequency — each factor seems reasonable in isolation
2. Tier eval frequency — a fast deterministic subset on every PR, the full LLM-as-judge suite less often
3. Use a cheaper judge for a first pass, escalating to a stronger judge only on ambiguous cases
4. Budget for eval cost explicitly, with its own attribution — it’s real infrastructure spend, not a rounding error

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Tags: agent evaluation, cost optimization, LLMOps, AI engineering