Fix Your Prompt Structure Before You Touch Your Infrastructure Most engineering teams treat LLM inference costs as an infrastructure problem. They evaluate model quantization, shop for cheaper GPU rentals, debate whether to move from GPT-4o to Claude Sonnet, and benchmark open-source alternatives. I have watched teams spend weeks on this and save fifteen percent. The same teams were running their system prompts with a timestamp in the first line and paying full token price on every single request. The optimization I am talking about is prompt caching. Anthropic charges $0.30 per million tokens for cache reads versus $3.00 per million for fresh input tokens — a 10x price difference for bytes the model already processed in the last hour. OpenAI applies automatic 50% discounts on cached tokens . The savings are not theoretical. They compound over every request your system makes, and most teams are not capturing them because they are breaking the cache themselves.…