How to reduce LLM costs in production
An LLM feature that looks cheap in a demo can get expensive fast at real volume. The good news: most production LLM bills have obvious slack in them. Here are the levers that cut cost without cutting quality.
1. Route to the right model
You do not need your most expensive model for every request. Route simple tasks (classification, extraction, short answers) to a smaller, cheaper model, and reserve the flagship for the hard cases. Model routing is often the single biggest saving.
2. Cache aggressively
Many applications ask the same things repeatedly. Cache responses for identical or near-identical requests, and cache expensive intermediate steps like embeddings. A Redis cache in front of your pipeline pays for itself quickly.
3. Fix retrieval so prompts are shorter
Stuffing huge context into every prompt is expensive and often makes answers worse. Better retrieval means you send only the relevant passages, which cuts token cost and improves quality at the same time. This is where good RAG saves money twice.
4. Be disciplined with prompts
Long system prompts run on every call. Trim them. Move stable instructions into a smaller, cached form. Small per-call savings multiply across millions of requests.
5. Use smaller or fine-tuned models where they fit
For a narrow, repetitive task, a smaller or lightly fine-tuned model can match a big general model at a fraction of the cost per call. We have shipped low-compute models (for example YOLOv8n for document detection) precisely because the lighter option was good enough and far cheaper to run.
6. Measure before you optimize
You cannot cut what you cannot see. Add observability so you know which calls, prompts and features drive the bill. Optimize the expensive 20%, not everything.
Cost control is not about one trick. It is routing, caching, retrieval and measurement working together, applied to the parts that actually cost money.
EpochC builds LLM systems that are cost-aware from day one. Start a project or hire on Upwork.