Originally published at theculprit.ai/blog/anthropic-prompt-caching-90-percent . LLM costs in production scale faster than the post-mortem of the demo bill suggests they will. The shape of the problem: you ship a feature that calls Claude on every meaningful event. The first month the bill is rounding error and nobody looks at it. The second month a customer's traffic ramps and the line item is suddenly five percent of revenue. The third month your finance person sends a polite Slack about whether this is "a real cost trend or a one-time spike," and everyone on the engineering team has to defend an architecture decision they made eight weeks ago when the bill was rounding error. You can reduce this. Not by being clever about how you call the model — by being clever about what's constant across your calls. Anthropic's prompt caching, in our case, takes the per-RCA input cost from full-rate to one-tenth of full-rate on a 90%+ cache-hit rate.…