I play too much Overcooked, a Nintendo Switch game in which cute animal chefs race to prepare meals before time runs out. One chops onions. Another stirs the soup. Someone washes dishes. Meanwhile, new orders keep arriving. Move too slowly and tickets pile up, customers wait, and the kitchen descends into chaos. Surprisingly, a kitchen is a good way to understand AI inference.
Building an AI product happens in two broad phases: training a model and putting that model to work.
Training is like sending a chef to culinary school. It is expensive and time-consuming, and the education may be specific to a cuisine. The chef studies recipes, practices techniques, and learns what works. Customers receive nothing while the chef is still in school. The benefit appears when the restaurant opens.
Inference is what happens in the kitchen after a customer places an order. You submit a question, image, or task to a model like ChatGPT, Claude, or Gemini. The model processes that new information, applies what it learned during training, and produces an answer. Every time an AI product drafts an email, detects fraud, summarizes a medical note, or helps a customer, it is performing inference.
Inference is measured in tokens: small pieces of information that a model reads and produces. A token may be a word, part of a word, or even a punctuation mark. In restaurant terms, tokens are like the ingredients flowing into the kitchen and the dishes coming out. The more tokens an AI system processes, the more computing capacity it uses — and the larger the bill can become.
Preparing one meal may be inexpensive. Serving millions of meals every day requires more chefs, more equipment, reliable kitchens, and a much larger operating budget.
As companies use more AI, inference will become a regular operating cost, much like software, internet access, or electricity. Without a clear view of their token usage, companies risk building products and workflows that cost too much to run or, worse, fail when demand surges.
Inference Exchange (IX) helps companies hedge changes in token prices and availability. Buyers can access many AI models and providers through one secure, compliance-ready marketplace. Sellers can resell tokens they no longer need. Contracts are flexible and settle in the actual tokens or capacity purchased — not cash. With IX, companies can keep their AI kitchens running without letting demand overwhelm their capacity or their budgets.