How to Cut Your LLM API Costs Without Hurting Quality
Nine practical, battle-tested ways to reduce LLM API spend — prompt caching, model routing, output discipline, batching and more — with the trade-offs spelled out.
Most LLM bills are 2–5× larger than they need to be, and almost none of the savings require a worse product. Here are the levers that actually move the number, roughly in order of return on effort.
1. Cap output tokens
Output is the expensive half of every request — often several times the input price. The single fastest win is setting sensible max_tokens limits and prompting for concise answers. Verbose responses are pure cost with no upside for most tasks.
2. Route by difficulty
Don't send every request to your most expensive model. A cheap classifier — or even a simple heuristic on input length and task type — can route easy requests to a fast, inexpensive model and reserve the frontier model for hard cases. This routinely halves spend on mixed workloads.
3. Cache the stable prefix
If your requests share a long, fixed prefix (a detailed system prompt, coding standards, a knowledge base), prompt caching bills those tokens at a fraction of the normal rate. The longer and more stable the prefix, the bigger the win. Model a realistic cached percentage in the cost calculator — not a best-case one.
4. Batch the offline work
Evaluations, backfills, bulk classification and nightly jobs don't need to be interactive. Batch tiers commonly apply a 0.5× discount in exchange for asynchronous delivery. If latency doesn't matter, this is close to free.
5. Trim the context you send
Retrieval-augmented setups often stuff far more context than the model needs. Tighter retrieval, deduplication and summarization of long histories cut input tokens directly. Measure how much of your context actually changes the answer.
6. Shorten conversation history
In chat-style apps, the full transcript is re-sent every turn, so cost grows quadratically with conversation length. Summarize old turns or window the history to keep per-turn input flat.
7. Pick the right model per task
Per-token price tells you little about per-task cost. A model that solves the task on the first try beats a cheaper one you call three times. Test on your real tasks and compare total calls, not sticker price. Our reviews of Claude and OpenAI get into this.
8. Consider self-hosting at high volume
Once volume is steady and high, self-hosted open-weight inference can undercut managed APIs — and it's the only option that keeps data fully in-house. It only wins at high utilization, so do the math first with our cost breakdown.
9. Measure before and after
You can't optimize what you don't track. Log tokens per request and cost per feature, then re-estimate with the LLM API cost calculator whenever your usage profile shifts.
Putting it together
Stack the cheap wins first: cap output, route by difficulty, cache the prefix, batch the offline jobs. Those four alone typically cut a bill by half with no quality loss. Re-run your numbers in the calculator after each change so you can see exactly what each lever bought you.