Model training vs inference
Training is when the AI learns. Inference is when it answers. Training happens once — or occasionally — and costs millions of dollars for large models. Inference happens every time someone asks a question, generates an image, or gets a recommendation, and costs fractions of a cent per interaction. When your vendor quotes you a per-seat or per-query price, that is inference cost. When they talk about fine-tuning a model on your data, that is a small additional training cost. Understanding this distinction matters because it shapes every buying decision: a model that is expensive to train but cheap to run is very different from one that is cheap to train but expensive at scale.
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Think of training like building a factory and inference like running it. GPT-4 reportedly cost over one hundred million dollars to train — that is the factory construction. But once built, each query costs fractions of a cent to process — that is the factory producing goods. This is why AI companies raise enormous amounts of capital upfront but can then serve millions of users relatively cheaply.
The economics create an important asymmetry. The company that trains the model bears enormous fixed costs. The company that uses the model pays variable costs per query. This is why the API pricing model works — you pay for what you use, while the provider amortizes their training investment across millions of customers.
For your business, the practical question is: do you need to train anything, or can you use an existing model? Most companies never need to train a model from scratch. They use pre-trained models through APIs and maybe fine-tune them with their own data — a process that costs thousands, not millions. The real cost you will manage is inference: how many queries, how complex, and which model tier you route them to.