
We Cut Our LLM Bill by 80% - And Never Touched the Model
This article explains how ROVA AI reduced its LLM costs by nearly 80% without switching to a cheaper model or compromising quality. The savings came from shorter prompts, concise structured outputs, prompt caching, avoiding unnecessary model calls, and batching non-real-time tasks. The key lesson: AI costs depend less on model choice and more on efficient token architecture.
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