I argued that foundation models beat custom forecasting pipelines at a certain breakeven point. Then a real benchmark landed on my desk. Here are the numbers: Chronos-2 vs a tuned XGBoost fleet vs a manually-reviewed supply chain reference, on monthly demand forecasting.
Amazon built two radically different approaches to predicting the future — a proprietary supply chain optimization pipeline (SCOT) and an open-source time series foundation model (Chronos). This post compares their architectures, trade-offs, and when each philosophy applies.
A decision framework for choosing between Amazon's Chronos-2 foundation model and custom XGBoost many-models pipelines for demand forecasting. Based on real patterns from SKU-level supply chain work.
Building a production forecasting application without the complexity of traditional ML model training and feature engineering.