Building Cost-Efficient LLM Pipelines: Caching, Batching and Model Routing

A practical guide to reducing LLM inference costs by 40-60% without sacrificing quality — using semantic caching, request batching and intelligent model routing. Includes full Python implementations, architecture diagrams and real pricing breakdowns. The moment an LLM-powered product gains traction, the invoices start arriving. A pipeline processing 500K requests per day at GPT-4o pricing can easily run $15,000-$25,000/month — and that number only climbs as usage grows. The reflex is to switch to a cheaper model, but that trades cost for quality in ways that surface as user complaints weeks later. There's a better path. Three techniques — semantic caching, request batching and model routing — can cut inference costs by 40-60% while maintaining (and sometimes improving) output quality. These aren't theoretical ideas. They're production patterns used in high-volume LLM systems across industries. This guide walks through each technique with full implementations, then shows how combining a