Tier your evals by cost: deterministic checks on 100% of outputs, LLM-judge only on samples

Towards Data Science·high signal

LLM-as-judge evaluation runs at roughly 30–50% of your inference cost because every scored output triggers another model call, so gate it: run near-zero-cost deterministic checks (schema validation, tool-call format, JSON parsing, length bounds, safety-filter passes) on every output, and reserve the expensive LLM judge for a sampled subset or the cases deterministic checks can't grade. The cheap layer catches the most common failures — malformed tool calls, truncation, invalid JSON — before you spend a judge token. For builders: a $4K/month inference budget can silently add $1.2–2K/month in eval cost if you LLM-judge everything; tiering keeps continuous evaluation affordable.