Token Economics: Cost-Optimizing LLM Applications

Every token in and out of a model is a line item on a bill that scales linearly with usage. An LLM feature that is cheap in a demo can become the single largest cost in your stack at scale, and the difference between a sustainable product and a runaway invoice is almost always architecture, not the price per token.

Every token is a cost — and a latency

Cost and latency move together: longer prompts and longer outputs both cost more and take longer. That alignment is convenient, because nearly every optimization that saves money also makes the product faster. You are not trading one against the other; you are pulling a single lever twice.

The levers that matter

Four moves cover most of the savings. Cache aggressively so repeat work never reaches the model. Compress context — retrieve and send only the chunks that matter, not the whole document. Tier your models — route the easy, high-volume tasks to a small cheap model and reserve the expensive one for genuinely hard work. And cap output length so a verbose model cannot quietly inflate every response.

// model tiering: pay for capability only when needed
const model = isComplex(task) ? "large" : "small";
const answer = await llm.complete(prompt, {
  model,
  maxTokens: 400,   // hard cap — no runaway responses
});

Measure before you optimize

You cannot cut what you do not track. Log token counts per request, per feature, per user, and the expensive paths reveal themselves immediately — usually a handful of routes generating the bulk of the spend. Optimize those, ignore the long tail, and the bill drops without touching most of the codebase.

The cheapest token is the one you never send. Caching, compression, and tiering are not micro-optimizations — at scale they are the business model.

The deepest lever here is caching; the Caching the AI teardown covers the architecture in full.