{"slug": "cut-your-llm-bill-by-30-to-70-the-levers-that-work", "title": "Cut your LLM bill by 30 to 70%: the levers that work", "summary": "A developer reports that LLM bills can be cut by 30 to 70% without degrading quality by addressing token waste. Key levers include caching near-duplicate calls and routing requests to the cheapest capable model. The first gains typically appear within two to three weeks.", "body_md": "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.\n\nIn 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.\n\nYes, 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.\n\nNo. 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.\n\nI never cut a bill by degrading quality. I cut it by no longer paying for what adds nothing.\n\nTaken 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.\n\n**How fast does the LLM bill drop?**\n\nThe first levers (caching, model routing) show results within two to three weeks.\n\n**Does cutting the bill mean degrading quality?**\n\nNo. 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.\n\n**Do I need to switch model provider to save?**\n\nRarely. Most of the savings come from how you call your current models.\n\nI write about shipping AI to production at [guinat.ai](https://www.guinat.ai). Honest advice, no hype.", "url": "https://wpnews.pro/news/cut-your-llm-bill-by-30-to-70-the-levers-that-work", "canonical_source": "https://dev.to/guinat_ai/cut-your-llm-bill-by-30-to-70-the-levers-that-work-2nmo", "published_at": "2026-07-12 07:54:52+00:00", "updated_at": "2026-07-12 08:14:14.865218+00:00", "lang": "en", "topics": ["large-language-models", "ai-infrastructure", "developer-tools"], "entities": ["guinat.ai"], "alternates": {"html": "https://wpnews.pro/news/cut-your-llm-bill-by-30-to-70-the-levers-that-work", "markdown": "https://wpnews.pro/news/cut-your-llm-bill-by-30-to-70-the-levers-that-work.md", "text": "https://wpnews.pro/news/cut-your-llm-bill-by-30-to-70-the-levers-that-work.txt", "jsonld": "https://wpnews.pro/news/cut-your-llm-bill-by-30-to-70-the-levers-that-work.jsonld"}}