Most teams overspend on AI not because models are expensive, but because usage is invisible. Without per-team attribution, duplicate prompts, and model selection defaults, costs compound silently until finance asks a question engineering can't answer.

The first step is instrumentation. Route all inference through a gateway that logs model, tokens, latency, team, and feature tags on every request. Within a week you'll know which workloads drive 80% of spend.

Next, apply routing rules. Not every task needs your most capable model. Classification, summarization, and extraction often perform equally well on smaller models at a fraction of the cost.

Finally, automate waste detection. Duplicate prompts, cache misses, and error retries are low-hanging fruit that platforms like Aethon surface automatically — often recovering 15–25% of monthly spend with no code changes.