Prompt Optimization: The Cheapest LLM Cost Cut You Are Not Making

Teams looking to reduce LLM API costs usually jump straight to switching models or negotiating volume discounts. The cheaper first move is almost always editing the prompt itself. Production prompts accumulate waste: polite filler ("Could you please...", "Thanks in advance!"), instructions pasted twice by different teammates, whitespace bloat from copy-paste, and stale context nobody trimmed. Every one of those characters is billed on every single request, forever. A prompt that carries 30% dead weight inflates your input bill by 30% at any scale, on any provider.

Order matters as much as length

Order matters as much as length. Providers award prompt caching discounts based on a stable prefix, so a prompt that interleaves fixed instructions with dynamic values (user questions, retrieved documents, timestamps) breaks the cache on every call. Restructuring so static content leads and variables trail can unlock cache-read rates of 10% to 50% of the normal input price without changing a word of meaning.

There is also a second-order effect: cleaner prompts run on cheaper models. Much of what teams attribute to "model quality" is really prompt ambiguity, and a tightly structured prompt often performs on a mid-tier model where the rambling original needed a flagship. Down-routing from a flagship to a strong mid-tier model is routinely a 5x to 15x price difference per token.

Doing the triage in one click

The prompt optimizer automates this triage in one click, entirely in your browser: it strips filler, deduplicates context blocks, reorders for caching, then shows before and after token counts, the annual dollar impact at your scale, and which cheaper models the cleaned-up workload could safely run on. Paste your ugliest production prompt and see what it finds.