Prompt Caching: The Quiet Feature That Makes LLM Apps Cheaper and Faster

This one https://www.anthropic.com/news/prompt-caching is the kind of infrastructure feature that does not get discussed much outside developer communities outside developer communities but that significantly changes the economics of building real applications with large language models.

The problem it solves: many production AI applications use the same long context repeatedly. A customer support assistant that includes a company knowledge base in every conversation. A coding assistant that includes a full codebase context. A legal analysis tool that includes a lengthy regulatory document. Without caching, every conversation pays the full token cost for the repeated context. With caching, the repeated portion is stored and reused, reducing both cost and latency.

The cost reduction being substantial on applications with long repeated context is the practical signal that makes this more than a developer convenience feature. For applications where the economics of LLM API calls determine whether the product is viable at scale, prompt caching can be the difference between a sustainable unit cost and an unsustainable one.

The broader pattern this represents is worth noting: as AI applications mature, infrastructure optimisation features become as important as model capability improvements for production viability. The question for builders is increasingly not just which model is best but which model plus infrastructure combination is best for your specific application economics.

For builders: is model quality or infrastructure cost the bigger constraint for AI application adoption at scale?