예측당 비용 추적하기

A monthly cloud bill hides which model wastes money. Cost per prediction turns spend into a unit you can compare, optimize, and defend.

Start with the basics: divide total serving spend by the number of predictions served. That ratio is your starting cost per prediction.

Compute is only part of it. A full picture adds storage, networking, and logging to capture the real total cost of serving.

You cannot split a shared bill without labels. Resource tags on instances and buckets let you attribute spend to each specific model.

Increment a counter on every served request so volume is never a guess. A prediction counter is the denominator your whole metric rests on.

An instance you pay for while it serves nothing inflates cost per prediction. Watching idle time reveals where money drains with no value returned.

Once each model has a cost per prediction, rank them. The most expensive model is your clearest optimization target.

Serving many inputs in one pass spreads fixed overhead across more results. Batching is often the fastest way to drop cost per prediction.

A number nobody sees changes nothing. Chart cost per prediction over time on a dashboard so regressions surface before the bill does.

Pick a target cost per prediction and alert when you cross it. A cost budget catches runaway spend the moment a deploy goes wrong.

Cheap is only good if the model still earns its keep. Weigh cost per prediction against the business value each prediction creates.

Let us confirm why this metric is worth tracking.

You computed cost per prediction, tagged resources, counted requests, compared models, and set a budget alert to keep spend honest. 📊