Training vs. Inference
Training = AI learns. Inference = AI works.
Explanation
During training, a model learns from data and builds its knowledge. In inference, it applies this knowledge to answer queries. Training happens once (or rarely), inference runs constantly.
How it works
Training: Millions of texts are processed, the model adjusts its parameters. Inference: A user asks a question, the model generates an answer in real time.
Example
GPT-4 was trained (training) for months. When you ask ChatGPT a question, it uses this training to answer (inference).
Why it matters
Helps to understand why AI models can be expensive to develop but inexpensive to operate.
Inference vs. training: what is the difference?
Training is the learning phase. A model processes huge amounts of data and adjusts billions of parameters until it reliably recognizes patterns. For large language models this takes thousands of specialized chips running for weeks or months, and the largest training runs are reported to cost hundreds of millions of dollars. Inference is the usage phase. The finished model receives an input and computes an output without learning anything new in the process.
For companies the distinction is practical: almost nobody trains a large language model from scratch. You use a pre-trained model and pay for inference, either per token through an API or through your own hardware. In between sits fine-tuning, a short additional training run on your own examples that costs a fraction of training from scratch.
Why inference drives the running costs
Training happens once per model version, inference happens on every single request. In production applications with many users, or with AI agents that make dozens of model calls per task, inference costs add up quickly. Reasoning models add to this: they “think” before answering and spend extra tokens on it, trading more compute at inference time for better results.
The levers for reducing cost therefore sit almost entirely on the inference side: smaller models for simple steps, caching of repeated context, shorter prompts and, at high and steady volume, your own hardware running a local LLM.
Frequently asked questions
What is inference in AI?
Inference is the process of running a trained model on new input to produce an output, such as a prediction or a generated answer. Every reply a chatbot gives is an inference call.
Is inference more expensive than training?
Per request, inference is cheap. Over the lifetime of a widely used model, however, total inference spend can exceed the one-time training cost, because it grows with every user and every request.
Does a model learn during inference?
No. The parameters stay fixed during inference. What looks like learning within a conversation is context in the prompt, which is gone in the next session unless it is stored and passed in again.