Most teams assume the fastest way to reduce AI costs is to switch to a smaller model. In reality, that's often the last thing you should do. Within a few weeks we noticed three problems:
API costs were increasing every day.
Response latency became inconsistent.
Most requests didn't actually need the most capable (and most expensive) model.
Downgrading to a cheaper model reduced costs, but it also reduced answer quality. Instead, we optimized the entire pipeline. The result was a significant reduction in token usage and API spend while keeping response quality almost unchanged. Here's what made the biggest difference.
