Cost-aware AI model routing chooses among available general models according to task needs and budget. The router may consider difficulty, quality history, latency, context capacity, privacy and inference price.
The cheapest model is not always the least expensive workflow choice if poor results create retries or human rework. Routing should be evaluated against accepted outcomes and total process cost.
ELI5
Cost-aware AI model routing tries to spend money where extra model capability matters most. It considers both the model's price and the likelihood that its answer will pass the checks needed to finish the task.
For example, a cheap model that succeeds on a tested database migration may be a good choice, while three failed cheap attempts at a hard bug can cost more than one strong-model attempt. The router should compare completed results, not just the price of the first response.
What factors can a cost-aware router consider?
It can consider task type, difficulty, quality history, latency, privacy, context size, availability and price.
Should routing always choose the lowest model price?
No. The route should minimize total useful-work cost, including failures, retries and human correction.
