Architecture decisions are documented as platform choices, not platform requirements. Data pipelines use formats and patterns portable across cloud and on-prem environments. Model artifacts are exported in standard formats such as ONNX or as pickled scikit-learn objects. GenAI applications abstract the LLM provider behind a service layer, so swapping foundation models is a configuration change. The result is portability without sacrificing platform-native performance.