The Economics of AI Inference at Scale
Inference cost can quietly become the line item that kills an AI product. The levers that keep it under control without sacrificing quality.
An AI feature that delights users in a demo can become an unsustainable cost at scale. Inference economics is the discipline of delivering quality at a price that makes the product viable, and it's an afterthought far too often.
Right-size the model
The largest model is rarely the right one. Many tasks are served just as well by a smaller, cheaper model — or by a smaller model fine-tuned on the task. Routing easy requests to small models and hard ones to large models is a major cost lever.
Cache aggressively
Identical and near-identical requests are common in real traffic. Caching responses, embeddings, and intermediate results avoids paying for the same computation twice. Semantic caching extends this to requests that are similar but not identical.
- Route requests to the smallest model that meets the bar
- Cache responses, embeddings, and intermediate results
- Batch requests to improve hardware utilisation
- Set budgets and alerts before scale, not after
Batch and autoscale
Hardware utilisation drives unit cost. Batching requests and autoscaling inference capacity to demand keeps expensive accelerators busy without over-provisioning. The goal is to pay for the compute you use, not the compute you might need.
Inference cost is a product decision disguised as an infrastructure one.
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