Whether a model's complexity is locked in before it sees a single row of data, or grows right along with the dataset.
A parametric model commits to a fixed functional form before it ever sees data — linear regression, for instance, is always "intercept plus slope times x," whether you fit it on 10 rows or 10 million. Its parameter count is fixed by that choice, not by the dataset, which makes it fast to fit and low-variance, but wrong wherever the true pattern doesn't actually look like the shape it assumed. A non-parametric model — k-nearest neighbors, decision trees, kernel methods — makes no such upfront commitment, and its effective complexity grows as more training data arrives. That flexibility lets it fit patterns a fixed-form model never could, at the cost of needing more data to do it safely and a much higher risk of fitting noise instead of signal.