Cutting Your LLM API Bill: What Worked, With Numbers

AI costs have a stubborn habit of blowing up quietly. The first proof of concept costs cents, then agents, retries, and "add an LLM to every feature" multiply the spend faster than the revenue grows. In 2026 the apps that keep AI affordable do not negotiate one cheaper model — they change how they feed the models. This guide covers the levers that matter most: semantic caching, routing by task difficulty, and token discipline, plus the hidden costs of re-sending context and generous output limits that most teams never total up. Quick Verdict You can usually cut runtime spend 30–60 percent without touching output quality, because the savings live in three things you are almost certainly doing wrong: re-answering the same prompt (caching), paying flagship prices for trivial tasks (routing), and sending a bloated context and oversized output on every call (discipline). Do caching, routing, and trimming first — they are cheap wins and they hold — before you downgrade to