Inference
What is Inference?
Running a finished model to get an answer, as opposed to training it in the first place. Every time you send a prompt and get a reply, that's inference. For most companies buying AI rather than building it, inference is where the ongoing running costs actually sit.
Why it matters
Training a model is a one-time cost. Inference is what happens every time you use the finished model, and for most companies buying AI rather than building it, this is where the ongoing bill actually sits. Every prompt sent and every reply generated is an inference, and each one uses computing power you pay for. That is the part people underestimate. A model that looks cheap in a demo can get expensive at scale, because the cost is per use and it never stops while the feature is live. This separates the one-off build cost from the running cost that grows with adoption.
In practice
A company adds an AI feature to its app and it works well, so usage climbs. The inference cost climbs with it, because every user interaction is another paid call to the model. Teams manage this by choosing a smaller model where it is good enough, keeping inputs tight and caching answers that repeat. The lesson is to budget for inference as a running operating cost, not a fixed one.
