LLM judging can cost 9.5x more than the chatbot it evaluates

On November 1, 2026, the AI section of DEV Community published a piece measuring how expensive LLM judges can get when they grade the output of a customer support chatbot. The scenario the author sets up is a familiar one: you have shipped a support chatbot built on GPT-5.6 mini, and you want to know how often it answers incorrectly. The usual advice, the author notes, is to let a stronger model grade every reply. The article then prices that setup. Each grading call costs 9.5 times as much as the reply it grades. At a volume of 10,000 replies per day, the bot itself costs $132 a month to run, while grading the full set would cost $1,260 a month, roughly an order of magnitude more than the deployment it is supposed to police. The mechanism, the author explains, is that model APIs bill per token, and a reply's token count comes to about three-quarters of its English word count, so reviewing a reply means feeding that same content through another model. The verdict is that most grades add little information that a sample of 400 would not, so the author recommends grading roughly 400 replies per day instead of the full 10,000. That cut alone would bring the monitoring bill down from $1,260 a month to a fraction of it, while still catching the answers that are actually wrong. The takeaway is that as far as cost is concerned, grading a reply costs more than writing it. The piece is a single practitioner's pricing exercise rather than a benchmark study, and it leaves the question of whether 400 samples a day is enough to characterize error rates across a customer base open, since the recommended sample size comes from the author's own experience rather than from a formal measurement of detection coverage.