Cutting LLM Costs Without Cutting Quality
Token bills grow quietly. Most of the savings come from architecture, not from haggling over model prices.
LLM costs are easy to ignore during a prototype and hard to ignore once a feature is popular. The instinct is to switch to a cheaper model everywhere, which often trades a small saving for a visible drop in quality. Better results come from looking at where the tokens actually go.
Measure first. Log input and output tokens per feature and per request type. In most applications a handful of prompts account for the majority of spend, usually because they include large system instructions or repeated context on every call.
Then shrink the inputs. Trim boilerplate from system prompts, retrieve fewer and more relevant chunks, and summarize long conversation histories instead of resending them. Prompt caching, where the provider supports it, makes repeated prefixes almost free.
Route by difficulty. Classification, extraction and routing decisions rarely need the most capable model. Use a smaller model by default and escalate to a larger one when confidence is low or the task is complex. Cache identical requests, especially for deterministic tasks with temperature zero.
Set budgets and alerts per feature so a runaway agent loop or a prompt regression is caught in hours, not at the end of the month. Cost is a quality metric; treat it like one.
