LLM Cost Management

LLM Cost Management covers the strategies and tooling used to control operational expenses in LLM-powered systems.

It includes token budget policies, model tiering (routing requests to cheaper models), prompt caching, batch API usage, and spend monitoring across providers. Teams that manage LLM costs actively can ship AI features at scale without runaway API bills. Also known as: LLM Cost Optimization

What this topic covers

Foundations — LLM Cost Management is counterintuitive: the most expensive tokens are often in the context window, not the completion.
Implementation — These guides walk through model routing, prompt caching, and batch API configurations — the three levers with the highest return on engineering time when controlling LLM spend at scale.
What's changing — Model pricing shifts constantly — new tiers, distilled models, and provider credits change which cost strategy wins.
Risks & limits — Aggressive cost-cutting creates hidden tradeoffs: cheaper models may degrade quality in ways users notice before engineers do, and shared caches can leak sensitive data between sessions.

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