Cost-Optimizing LLM API Usage Without Sacrificing Quality

The instinct when an LLM bill gets too high is to downgrade to a cheaper model across the board, and that's usually the wrong first move — it degrades quality on the hard cases while barely touching cost on the easy ones, because the easy cases were never the expensive part. We start by breaking down spend per request type, and it's almost always a small number of call patterns driving most of the bill.

For one client, 40% of monthly spend came from a single feature that re-sent the full conversation history on every turn, including large retrieved documents that hadn't changed. Trimming that context — summarizing older turns and only re-sending documents when they changed — cut that feature's cost by 65% with no measurable drop in output quality on our eval set.

Model routing did more for us than any single model swap. Cheap, fast models handle classification and simple extraction; the expensive model is reserved for the steps that actually need its reasoning. On a document pipeline, routing 80% of requests to a smaller model and reserving the frontier model for the 20% flagged as ambiguous cut total cost by 55% while keeping accuracy on the hard cases unchanged.

Prompt caching for repeated system prompts and few-shot examples was the easiest win we've shipped — near-zero engineering effort, and for high-volume endpoints with a stable system prompt it routinely cut input token costs by 30-40% once cache hit rates stabilized.

We're careful to tell clients that cost optimization is an ongoing job, not a one-time project — usage patterns shift, new features get added without anyone revisiting the cost model, and the cheapest architecture six months ago is rarely the cheapest one today. We build cost dashboards into the same observability stack we use for quality, so both get reviewed together instead of cost being a surprise at the end of the month.