A Practitioner's Framework for Enterprise AI Cost Optimization: Token Efficiency, Model Routing, Caching Architecture, and Agentic Cost Control

Abstract

The rapid enterprise adoption of large language model (LLM) APIs has introduced a new class of operational expenditure that most organizations are ill-equipped tomanage. Unlike traditional infrastructure costs, LLM spend is consumption-based, usage-nonlinear, and highly sensitive to architectural decisions made at the promptand system design level. This paper presents a unified, practitioner-validated framework for enterprise AI cost optimization spanning five intervention layers: (1) tokeneconomics and prompt compression, (2) task-aware model routing, (3) caching architecture including semantic and prompt caching, (4) Retrieval-AugmentedGeneration (RAG) context management, and (5) agentic pipeline cost control. Through analysis of representative enterprise workloads across SaaS, legal technology,and financial services verticals, we demonstrate that systematic application of this framework achieves cost reductions of 60–92% without statistically significantdegradation in output quality. We further propose eight LLM FinOps metrics as a standardized measurement vocabulary for ongoing cost governance. This frameworkis intended to serve both as an operational guide for engineering teams and as a consulting engagement structure for AI optimization practitioners.