Creating a model is a large one-time effort, consuming enormous computation to learn from data. Running it to answer requests, called inference, is cheaper per use but happens constantly, so over a model's life the running cost often dwarfs the training cost.
This shapes design choices: it can be worth spending more to train a smaller, efficient model if it will serve millions of requests. Understanding the split explains why so much engineering effort goes into making inference faster and cheaper.