The latest articles on DEV Community by Nathan Guihot (@guinat_ai). https://dev.to/guinat_ai https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4025811%2F04b711d2-52d4-4a53-ad44-a1272e3cac97.png https://dev.to/guinat_ai en Nathan Guihot Sun, 12 Jul 2026 07:54:52 +0000 https://dev.to/guinat_ai/cut-your-llm-bill-by-30-to-70-the-levers-that-work-2nmo https://dev.to/guinat_ai/cut-your-llm-bill-by-30-to-70-the-levers-that-work-2nmo <p>On the bills I audit, the problem is almost never the price per token. It is useless context sent on every call and the most expensive model plugged in everywhere by default. Here is what I cut first.</p> <h2> Why is my AI bill exploding when usage isn't going up? </h2> <p>In nearly every engagement, the problem is not the price per token but the way the tokens are spent: useless context sent back on every call, the most expensive model used everywhere by default, answers regenerated when they already existed. So I start by measuring where each euro goes, not by cutting at random.</p> <h2> Can caching really cut my LLM bill? </h2> <p>Yes, and it is almost always the first lever: in production, a large share of calls are near-duplicates. Caching the answers on identical inputs removes that waste without changing anything for your users, often within a few days.</p> <h2> Should I use the same model for every task? </h2> <p>No. Routing each request to the cheapest model capable of handling it is enough in most cases: a simple classification or extraction does not need the most powerful model.</p> <blockquote> <p>I never cut a bill by degrading quality. I cut it by no longer paying for what adds nothing.</p> </blockquote> <ul> <li>Cut the dead context: everything the model never reads still costs money.</li> <li>Favor short instructions and targeted examples over long directives.</li> <li>Group bulk processing into batches when latency allows.</li> </ul> <h2> How much can I really save, and how fast? </h2> <p>Taken together, these levers bring a bill down by 30 to 70% on most of the products I audit, at equal quality, and the first gains usually land within two to three weeks.</p> <h2> FAQ </h2> <p><strong>How fast does the LLM bill drop?</strong><br> The first levers (caching, model routing) show results within two to three weeks.</p> <p><strong>Does cutting the bill mean degrading quality?</strong><br> No. I remove the useless context, the duplicates and the use of the most expensive model where a lighter one is enough. The quality measured by my tests stays stable, only the cost goes down.</p> <p><strong>Do I need to switch model provider to save?</strong><br> Rarely. Most of the savings come from how you call your current models.</p> <p>I write about shipping AI to production at <a href="https://www.guinat.ai" rel="noopener noreferrer">guinat.ai</a>. Honest advice, no hype.</p> ai llm programming machinelearning