Semantic Caching for LLMs: Cut Your Token Bill in Python

On June 26, 2026, CNBC ran a story that landed hard across every engineering channel: the era of tokenmaxxing is over. Uber admitted it burned through its entire annual AI budget in four months. Lindy moved 100% of its traffic off Claude to a cheaper model because, in its CEO's words, it was "a matter of survival for the business." The message for everyone shipping LLM features is blunt: spend-at-all-costs is finished, and cost per request is now a first-class engineering metric.

Most of the advice you'll see is about routing: send easy work to a cheap model, escalate the hard stuff to a frontier model. That helps. But there's a cheaper lever that a lot of teams skip, and it's the one that pays off first in production: don't call the model at all when you already answered the same question a minute ago.