Most teams overspend on LLM APIs not because the models are expensive, but because nobody owns the bill. A few structural changes cut costs by half without touching output quality.
Route tasks to the cheapest capable model
Classification, extraction and short summaries do not need a flagship model. Route them to a small model and reserve the expensive one for reasoning-heavy steps. A two-tier routing setup typically moves 70 percent of the volume to the cheap tier.
Trim the prompt, not the quality
Input tokens are billed too. Audit your system prompts quarterly: boilerplate instructions, dead examples and redundant context accumulate silently. Cutting a 3,000-token system prompt to 1,200 tokens saves money on every single call your application makes.
Finally, cap output length explicitly. Models happily generate long answers when the task needs three sentences, and output tokens are the expensive ones.
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