Inference vs. Training: What Happens When You Actually Use an AI Model
Training happens once (or occasionally); inference happens every single time you send a message. Here's the difference and why it matters for cost and speed.
Two very different phases
Training is the (extremely expensive, often months-long) process of adjusting a model's parameters on a huge dataset. Inference is simply running the already-trained model on a new input to get an output — what happens every time you send a prompt to a chatbot. They use the same model, but they're completely different operations.
Why inference cost is what you actually pay for
As a user or developer, you almost never pay for training — that cost was already absorbed by the provider. What you pay per request is inference cost: the compute needed to run your specific prompt through the model once. That's why larger models (see Choosing the Right Model Size) cost more per request — more parameters means more compute per inference.
What determines inference speed
Response speed depends mostly on model size, output length (longer answers take longer to generate, since text is produced one piece at a time), and how much competing traffic the provider's infrastructure is handling at that moment.
Frequently Asked Questions
What is inference in AI?
Running an already-trained model on a new input to produce an output — what happens every time you send a message to a chatbot, as opposed to training, which happens beforehand to build the model.
Why do bigger models cost more per request?
Because inference cost is driven by how much compute is needed to run a single request through the model, and larger models (more parameters) need more compute per request.