12 Ways to Reduce LLM Latency and Inference Costs in Production

Large language model (LLM) apps get slow and expensive faster than you'd expect. In a prototype, things look fine. A few users, one model call, a short prompt, and response times you don't think twice about. Production is a different story. Traffic spikes and requests pile up in a queue. Conversations get longer. Retrieval-augmented generation (RAG) pipelines add big chunks of context to every prompt. Agents call several tools instead of one. And those generous output limits you set early on quietly push up both latency and cost. The surprising part is that the fix usually isn't a better model or more graphics processing units (GPUs). Most of the gains come from cutting work you didn't need to do in the first place: fewer tokens, fewer calls, a smaller model for the easy tasks, real cache reuse, and less time stuck in a queue. This guide covers 12 practical ways to cut LLM latency and inference cost in production. So, let's get started: #…