The same feature, computed two different ways in two different places, gives two different answers — training pipeline code and serving pipeline code drift apart, quietly, until a model that scored well offline behaves differently in production for no reason anyone can immediately find.
Choosing between an overnight batch job and a 50-millisecond synchronous API is not a minor implementation detail — it changes almost every other decision downstream, from infrastructure to model architecture to error handling. The serving pattern should be chosen deliberately, before the model is designed, not bolted on after.