past couple of years have seen a surge of investment in open‑source and commercial tabular foundation models built around in‑context learning (ICL). In 2025, for example, the software giant SAP released the SAP-RPT-1 suite of models, targeting ERP-centric tasks in areas such as financial planning, sales and procurement order processing, and supply chain management. Unlike traditional supervised machine learning – where models are trained and fine-tuned for specific tasks – ICL allows a single, generically pretrained model to adapt on the fly using relatively small amounts of task-specific data provided in the context payload , which acts as a kind of ephemeral training set. While the shift to ICL eliminates the need for costly (re)training of task-specific tabular models, it introduces an important accuracy-latency trade‑off at inference time, especially for centrally hosted models like SAP-RPT-1.…