Scaling in AI refers to the observation that model performance improves predictably as you increase compute, data, and parameters. This principle, sometimes called scaling laws, has driven the race to build ever-larger models. But scaling is not just about bigger models: it also means scaling operations, deploying AI to more users, more use cases, and more regions without breaking reliability or breaking the bank.