Cost Management › Cost Management

Deploying language models at scale is expensive. A single GPU instance hosting a large model can cost several dollars per hour, and a production system serving thousands of users may require dozens of such instances. Without deliberate cost management, teams often discover that inference costs dwarf development expenses, and the economics of an otherwise promising application collapse under real-world load.

Cost management for LLM deployments covers four interconnected concerns. Cost modeling builds a quantitative understanding of where money goes and what drives spending. Cost optimization reduces spending through architectural choices, hardware selection, and inference techniques. Cost allocation answers the question of which users, teams, or features are responsible for which costs. And cost monitoring creates the visibility needed to detect anomalies, enforce budgets, and continuously improve.

These concerns do not exist in isolation. A meaningful cost optimization initiative requires first understanding the cost model. Effective allocation requires instrumentation that also enables monitoring. This chapter walks through each area in depth, building from the fundamentals of LLM economics to practical techniques you can apply to real production systems. It builds on the inference optimization techniques covered in earlier chapters, including KV caching, quantization, continuous batching, and speculative decoding. Those techniques reduce the per-request cost; this chapter shows how to measure, attribute, and manage that cost systematically.