Prompt Caching and the New AI Efficiency Race

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The growing complexity of AI memory management is becoming visible even at the application layer. For example, documentation around prompt caching from Anthropic has evolved from simple cost-saving guidance into a detailed playbook on pre-purchasing cache tiers and optimizing read-write strategies for models like Claude.

Because cached memory makes repeat queries significantly cheaper, companies that carefully manage cache windows — whether five minutes or an hour — can slash operating costs. Meanwhile, startups like Tensormesh are building tools to optimize cache layers deeper in the stack. As models become more token-efficient and memory orchestration improves, inference costs are expected to fall — potentially unlocking AI applications that today seem financially out of reach.

[johnblockbuster]

funny how at the top it’s parliament worrying about sovereignty, and at the startup level it’s founders worrying about token burn. same tech, very different stress.