LLM cost is invisible during a pilot and suddenly noticeable in production. The reason is simple: a hundred calls in the pilot, a hundred thousand in production.
01. Where the Cost Comes From
You pay per token sent and received. Long prompts, long documents and long answers land directly on the invoice. Two designs doing the same job can differ fivefold.
02. Shortening the Prompt
The fixed instructions sent on every call bloat over time. Reviewing them regularly and cutting unnecessary sentences is direct saving at volume.
03. Caching
If the same or similar questions recur, store the answer. Prompt caching offered by providers for the fixed system instruction also lowers the cost of repeated input.
04. Pre-Filtering
Placing a rule-based filter before the model dramatically reduces how many records get processed. In document classification flows this is usually the single highest-return step.
05. Constraining Output
Limit answer length and ask for structured output. Free text is both expensive and hard to parse in the next step.
06. Measure and Alert
Compute cost per unit of work: per document, per request. Tracking unit cost rather than a monthly total lets you tell an increase apart from volume growth. Set an alert on the budget threshold.