Strategies to reduce AI production costs: model distillation, intelligent routing, context optimization, and monitoring tools. The product manager playbook for AI unit economics.
AI FinOps is the discipline of managing and optimizing the cost of operating language models in production. It includes monitoring token spend, routing requests to cheaper models when possible, distilling large models into smaller ones, and optimizing context to reduce the volume of tokens processed.
Shopify reduced costs 30x with model distillation. RidgeText reduced context from 125K to 150 tokens with in-memory layers. Frugon identifies calls that can move to cheaper models. The three levers: the right model for each task, optimized context, and intelligent routing.
At scale, token costs can make an AI product unviable. If each call costs $0.01 and you have 1 million active users making 10 calls per day, that is $100K per day in tokens alone. Unit economics need to work with a healthy margin, which requires aggressive inference cost optimization.
The discipline of managing and optimizing the cost of operating language models ...
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