If you have a spreadsheet of customers, transactions, or signups and you want to predict something from it — who will churn, which lead converts, what next month's number is — your instinct is probably to reach for XGBoost or to paste the rows into a chatbot. As of this year there's a third option that beats both on the most common case, and it needs no training and no data scientist.

A tabular foundation model (TFM) is a transformer pretrained once on millions of synthetic tables, so it can predict on your table in a single forward pass — no per-dataset training, no hyperparameter tuning. You hand it your labeled rows and the rows you want answered; it returns predictions and calibrated probabilities in seconds. The leading one is TabPFN, from Freiburg's Prior Labs — and on July 17, 2026, SAP closed its acquisition of Prior Labs, committing more than €1 billion to scale it into a frontier lab for business data (we put that deal in context in this week's Founder's Wire). That's the signal worth reading: the company that runs the world's ERP tables just decided a foundation model for structured data is as strategic as an LLM is for text.

Here's how to pick between the three tools you'd actually consider.

The three options, in one line each#

When TabPFN wins (and when it doesn't)#

TabPFN-2.5, released November 6, 2025, handles up to 50,000 rows and 2,000 features for both classification and regression — a 20× jump in data cells over the previous version. On the independent TabArena benchmark it's the leading method, and it matches AutoGluon 1.4 — a four-hour tuned ensemble — straight out of the box with no tuning. Against a default XGBoost, Prior Labs reports a 100% win rate on datasets up to 10,000 rows and 500 features, and an 87% win rate on larger sets up to 100K rows / 2K features.

The catch is the ceiling. Past ~50,000 rows or 2,000 features, accuracy degrades — that's the regime where gradient-boosted trees still own the field. TFMs are also structured-tables-only: no text, images, or audio. And if you need CPU-only millisecond inference at high volume, or hard feature-importance interpretability for a regulator, trees remain the pragmatic pick.

Why not just ask an LLM?#

Because an LLM on a CSV is a reasoner, not a predictor. It's excellent for "what stands out in this data?" or "write the pandas to segment these users," and it will happily explain a result. But it gives you no calibrated probabilities, its numeric answers can be confidently wrong, it doesn't scale past a few hundred rows in a prompt, and dropping customer data into a chat product is a privacy exposure you don't need. Keep the LLM for the sentence around the prediction, not the prediction.

Try it in five lines#

TabPFN's weights are open (Apache-licensed, on Hugging Face and Kaggle); a GPU helps but a hosted client exists if you don't have one.

pip install tabpfn

from tabpfn import TabPFNClassifier   # or TabPFNRegressor for a number

clf = TabPFNClassifier()             # no config to tune
clf.fit(X_train, y_train)            # "fit" just stores the context — no training
preds = clf.predict(X_test)          # single forward pass
proba = clf.predict_proba(X_test)    # calibrated probabilities, for free

That's the whole workflow. No experiment tracking, no sweep, no model registry — the reason it's suddenly interesting to a team of one.

The founder's decision#

The larger point behind SAP's €1 billion: most of the world's decisions still run on tables, not prose. A foundation model that reads those tables cold is now good enough that the fastest path from a spreadsheet to a real prediction no longer runs through a data-science team. If that's you, it's five lines away.