AI training is the process of teaching a machine learning model to recognize patterns by feeding it large amounts of data. Think of it as the learning phase — like a student studying a subject over time.
During training, an AI model is presented with inputs (such as images, text or sensor data) along with the correct outputs (like labels or answers). The model then adjusts its internal parameters — essentially the “knobs” and “dials” of a neural network — to reflect the relationships between the inputs and outputs as best it can.
For example, training a large language model (LLM) like OpenAI’s GPT series of LLMs involves showing it billions of sentences from books, websites and articles, and having it predict the next word in a sentence. Over time, it “learns” the structure, grammar and meaning of language.
This learning is internalized in the model. Now, the model is ready for the next stage: inference.
AI inference is the activity of taking new data and giving it to the trained model to draw conclusions. Since the model has learned from its training dataset, it can apply those learnings to new data it gets.
Going back to the student example, it is similar to the pupil taking an exam to answer questions based on what they’ve already learned.
During inference, the model receives new input (new text prompt or image) and uses what it learned during training to generate an output (identifying an animal or summarizing an article).
For example, when you type a prompt into ChatGPT, the model is performing inference. It’s using its trained knowledge to generate a response in real time, without learning anything new from your specific prompt.
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Why the Difference Matters
Businesses spooked by headlines about the billions of dollars spent on AI training can rest assured that these costs are mostly spent by AI developers such as OpenAI and Google. Companies don’t have to train an AI model from scratch, unless they want to for their own purposes.
Instead, what’s most useful to companies is the inference stage — taking a pretrained AI model and then inputting their own data or prompts to help them perform business tasks more efficiently.
For example, AI inference use cases include generating images for a marketing campaign, summarizing meeting notes, finding a new drug, researching legal cases and many others.
Inference generally is cheaper than model training, since companies don’t have to come up with massive volumes of data, lease costly hardware like Nvidia GPUs, or tap large cloud compute clusters — although they still need GPUs or AI accelerator chips to minimize latency.
However, while inference is cheaper per request, it can become expensive at scale depending on how many users access the model regularly. This is a cost that companies using AI can control better.
AI training is a one-time cost for the model developer; in AI inference, every prompt generates tokens, which incur a cost each time. But prices have been coming down: The inference cost for a system performing at the level of GPT-3.5 has fallen 280-fold in two years ending October 2024, according to Stanford University’s 2025 AI Index Report.
AI training happens in the cloud, while inference can happen in the cloud, on-premises, or in edge devices like smartphones or autonomous vehicles.
Recent advances in AI, however, are starting to blur the line between training and inference.
Some new approaches, like reinforcement learning, let models keep learning after deployment. Meanwhile, innovations in AI hardware are making both training and inference faster and more efficient.
