Originally published on AI Tech Connect.
Why caching is the cost lever most teams miss Walk into any growing AI team in Bengaluru or London and ask what their largest controllable infrastructure cost is. The honest answer, more often than not, is tokens — specifically, the same expensive context being re-sent on every call. A retrieval-augmented chatbot pasting the same 40k-token system prompt and tool schema into every turn. An evaluation harness re-uploading 200k tokens of test cases for each model variant. A coding agent that reads 800k tokens of repository context on every single edit. Every major vendor now offers a way to make those repeated prefixes effectively free, or at least drastically cheaper. The mechanics differ, the surface area differs, and the gotchas differ — but the underlying idea is the same: the model has…
