Why does it feel like big LLM providers are literally hiding prompt caching?

I know the info is there. Somewhere in the pricing pages, docs, or API notes. But for something that can seriously change what you pay in production, it is weirdly under-explained. expeciely for other providers than openai which they do have decent explainer here - https://developers.openai.com/api/docs/guides/prompt-caching So basicly: two prompts can look almost identical, but one can be much cheaper to run just because it is ordered better. Put the changing parts too early, like the user query, variables, timestamps, metadata, or anything request-specific, and you can break the stable prefix the cache depends on. The practical rule is simple: Keep the repeatable stuff first. Start with system instructions, fixed rules, examples, schemas, and formatting requirements. Then put the dynamic user input and request-specific data near the end. That is it. Just a good prompt structure... But if you run LLMs at scale, this tiny detail can be the difference between insanely expensive LLMs usage and acctually good ROI product. full blog post here