Token Cost Optimization in Production LLMs: 3 Approaches With Real Numbers

We were burning $4,100/month on inference for one fintech client. Here's the three-part stack that cut it to $1,560, without touching the model. LLM inference costs are the silent budget killer of production AI. You see a demo that costs pennies to run. You ship it, users arrive, the corpus grows, query complexity rises — and suddenly you're looking at a cloud bill that nobody planned for. We hit this on a fintech client's internal compliance Q&A system. At launch: ~2,000 queries/day, average prompt length 1,800 tokens, GPT-4 for everything. Monthly inference bill: $4,100. Three months post-launch: 6,000 queries/day, average prompt ballooning to 2,400 tokens from accumulated context. Projected bill: $13,000/month. Nobody had modelled for usage growth. Here's the three-layer optimization stack we implemented, with exact numbers from that engagement. 01 Prompt compression — trim the fat before it hits the model The most direct lever: reduce the token count of every prompt before it r