A SaaS company's AI-powered document analysis feature was costing $25,000 per month in API calls. At their growth rate, they'd hit $150,000 monthly within a year, a cost that threatened to make the feature unprofitable.

Their engineering team made four changes. First, they added a caching layer, 30% of queries were near-duplicates that could be served from cache. Second, they switched from their premium model to a mid-tier model for the initial document classification step, which performed identically for that narrow task. Third, they optimized their prompts to use 40% fewer tokens while maintaining output quality. Fourth, they batched non-urgent analyses into off-peak windows at lower rates.

The result: monthly costs dropped from $25,000 to $5,200, a 79% reduction with no measurable decrease in output quality. The feature went from margin-threatening to their most profitable product line. AI cost optimization isn't just an engineering concern. It's a business strategy skill that directly impacts profitability.