How One Engineering Team Tripled Its AI Coding Bill and Clawed Costs Back

A small software company's AI coding expenses tripled in a matter of months after engineers fully adopted tools like Claude Code and Codex, with no visibility into where the money was going. The engineering lead identified three core blind spots: no per-user or per-task usage breakdown, no controls to stop runaway processes, and no governance over which AI model was selected for each task. A single developer was consuming more tokens than five colleagues combined, simply because their workflow defaulted to the most expensive model for routine tasks like documentation and commit messages. Enabling prompt caching proved the highest-impact fix, since agentic tools repeatedly send the same context and can read cached tokens at a fraction of the full input price. The team also found that cheaper, smaller models handled many routine tasks at identical quality, highlighting that model choice — often made automatically by the tool — has a major cost impact teams rarely scrutinize.