How One Team Cut AI API Costs by 84% Using Model Routing and Caching

A backend engineering team discovered their monthly LLM spending had ballooned to $11,400, roughly three times their projected budget, largely because they defaulted to GPT-4o for every task. After three weeks of cost analysis, the team found that for 85–95% of production requests — including classification, summarization, and simple chat — cheaper models performed comparably in blind tests. Switching to task-specific models such as DeepSeek and Qwen variants, without any additional optimization, reduced the bill to approximately $2,900, a 75% drop. The team then implemented a routing layer that maps each task type to the most cost-effective model, with GPT-4o-class models reserved only for the minority of requests where higher reasoning is demonstrably necessary. The engineer estimates the combined strategies ultimately brought monthly spend down to $1,830, an overall reduction of about 84%.