7 Ways to Cut Your LLM API Bill Without Losing Quality
Most teams overspend on LLM APIs for the same few reasons. These seven changes usually give the biggest savings.
1. Use the smallest model that works
Moving a task from a top model to a small one can cut cost by 20 times or more. Test cheap models first on classification, extraction and routing.
2. Route by difficulty
Send easy requests to a cheap model and escalate only the hard ones. Even a simple rule, such as short inputs to the small model, helps.
3. Cache repeated context
If every request starts with the same long instructions or document, cached input costs a fraction of normal input. See prompt caching explained.
4. Batch what is not urgent
Batch APIs are about half price. Reports, tagging and nightly jobs rarely need instant answers. See batch API pricing.
5. Trim your prompts
Remove unused instructions, examples and old conversation turns. Every token is billed on every call.
6. Cap output length
Output tokens are the expensive half. Ask for concise answers and set a maximum output length.
7. Measure before you optimize
You cannot cut what you cannot see. Log tokens, model, team and project for each call. The AI spend tracker template gives you a ready structure.
How the savings stack
Start with our standard workload of 45 million input and 12 million output tokens a month on Claude Sonnet 5.5, about $210. Route 80% of requests to Claude Haiku 5.5 and keep 20% on Sonnet, and the cost falls to about $50. If half of that traffic is not urgent and can go through a batch API at half price, the bill drops to roughly $38. That is an 82% reduction without changing the user experience for the requests that matter.
Small changes that add up
Shorter system prompts. Cutting a 2,000-token system prompt to 800 tokens, across 30,000 requests a month, saves 36 million input tokens. On a $2 per million model, that is about $72 a month.
Output caps. Trimming the average answer from 400 to 300 tokens saves 3 million output tokens over the same 30,000 requests. At $10 per million, that is about $30 a month.
Less history. In chat apps, send a summary of old turns instead of the full transcript. Long histories are a hidden multiplier, since the entire conversation is billed again on each turn.
The order to tackle them
1. Measure usage by model and feature, so you know where the money goes.
2. Test a cheaper model on your most expensive feature.
3. Add caching where requests share a long prefix.
4. Move non-urgent jobs to batch.
5. Trim prompts and cap outputs.
6. Review monthly, because prices and models change.
Start with the biggest line item. Optimizing something that is 2% of the bill is not worth an afternoon.
Quality guardrails
Savings mean little if quality drops. Keep a test set of real examples with expected results, and re-run it every time you change a model or prompt. Track the share of answers that pass. If a cheaper setup keeps the pass rate within what you can accept, ship it. Also watch for hidden costs such as retries, which can erase a price advantage.
Build the habit, not a project
You do not need heavy tooling. A simple log of date, team, model, tokens and cost, reviewed once a month, catches most waste. The AI spend tracker template is built for exactly this.
Frequently asked questions
What is the biggest single saving?
Switching a high-volume task to a smaller model, which can cut that task's cost by an order of magnitude.
Can I combine caching and batch?
Often yes, but rules differ by provider, so read the documentation before you plan on both.
How much time does optimization take?
A first pass, measuring and swapping one model, can take a day. The ongoing monthly review takes less than an hour.
Quantify the savings
Use the cost calculator to compare your current model against cheaper options before you change anything.
Track spend by team and project: the AI Spend Tracker spreadsheet is launching soon.