What Actually Drives LLM API Cost, and Where Prompt Optimizer Fits

Most of the API bill comes down to three independent levers: how much of your traffic is duplicate, how much of it is simple enough for a cheaper model, and how verbose your prompts are. Prompt Optimizer addresses the first two directly. The third is more nuanced than "shorter is cheaper" — worth being precise about.

Duplicate requests: zero-cost by definition

Caching identical or near-identical requests is the easiest win — a cache hit costs nothing. How much this saves you depends entirely on how repetitive your traffic is; FAQ-style workloads benefit a lot, novel requests don't benefit at all.

Simple requests: route them away from frontier models

Prompt Optimizer's 3-tier router classifies each request before optimizing it. In a 360-prompt production sample, roughly a quarter resolved on the rules tier alone — deterministic pattern matching, no LLM call, no latency, no cost. The rest split between a lighter hybrid tier and, rarely, a full frontier-model rewrite. This is the most reliable cost lever, because it's architectural: you're simply not paying for a model you didn't need.

Prompt optimization: a quality lever, not a compression lever

Here's the part worth being honest about. We ran a real audit — 360 production prompts, measured with an actual tokenizer, before and after optimization. 354 of the 360 got longer, not shorter. Every context category expanded on average, including code generation. The reason is straightforward: the optimizer adds the structure a bare prompt is missing (role, constraints, output format, edge cases) — and structure costs tokens.

The exception is prompts that are already verbose or repetitive — padded politeness, restated context, long chat histories with duplicated information. Those can shrink. But if your baseline prompt is already reasonably tight, expect it to grow, not shrink, after optimization.

The honest takeaway

If you're evaluating Prompt Optimizer for token-count reduction specifically, don't — evaluate it for output quality, consistency, and the routing/caching layers, which are the parts that actually reduce cost. Try it at Prompt Optimizer and judge the before/after on quality, not token count.