Cutting LLM Costs Without Losing Quality

LLM bills grow quietly, and the reflex response is to switch to a cheaper model everywhere. There are usually two or three larger savings available before quality has to be traded at all.

Trim the context first

Most of the cost is input tokens, and most input tokens are unnecessary: an entire conversation history resent on every turn, ten retrieved documents where three were relevant, or a system prompt that grew by accretion. Reranking retrieved results and summarising older turns often halves the bill with no change in output.

Cache the repeated parts

If a long system prompt or document set is sent with every request, prompt caching charges for it once rather than every time. For high-volume applications with a stable preamble, this is frequently the single largest saving available.

Route by difficulty

Not every request needs the strongest model. Classify simple requests to a smaller, cheaper model and escalate only the hard ones. Validate the routing with your evaluation set so you can see exactly what the escalation threshold costs in quality.

Instrument cost per request by feature before optimising. The expensive path is usually one endpoint nobody suspected, not the system as a whole.