Training builds a model once, often over days or weeks. Inference happens every time the model is actually used: a support ticket comes in and the model reads it and produces a category, or a user asks a question and the model drafts an answer. Inference is where most AI cost and latency in production actually come from, since it happens on every request rather than once.
The common confusion is treating inference speed and training speed as the same problem. A model can take weeks to train and still answer in under a second, because inference uses the already trained model rather than adjusting it. Businesses planning AI costs need to estimate inference volume, since that is the cost that scales with usage, not the one-time training cost.