A demo runs once. A pipeline runs at 3am, forty times, on a box you're paying for by the second.
This is the part that decides whether zero-shot ML actually ships: what it costs to run, where the cliffs are, and how to deploy it somewhere with no internet.
In the last post we said switching foundation models is one keyword. So we switched it fifteen times and wrote down what happened.
The short version: for classification, model choice barely moved the needle. For regression, it moved it by 30%. The model that won most often is the one you're least likely to be allowed to use. And when we ran scikit-learn on the same splits, the foundation models won all five — by 2–7%, for up to 550× the compute.
You have 120 labelled rows and 30 unlabelled ones. The classical answer is a Python project. The new answer is one SELECT.
SELECT * FROM tabfm_classify('iris', 'species');
No training loop. No feature engineering. No model artifact to version. The model reads your labelled rows as context and predicts the rest—the same way an LLM learns from examples in a prompt.