You have seen tradeoffs as a recurring theme across every book in this series: time versus space in algorithms, consistency versus availability in distributed systems, coupling versus cohesion in code architecture, autonomy versus coordination in engineering leadership. At each layer, the tradeoff framework is the same — identify what you gain, identify what you give up, define the conditions under which the exchange is correct. This chapter applies it to the AI product layer, where the relevant dimensions are quality, latency, cost, and controllability. No architecture optimises all four simultaneously. Every design choice is a point in this four-dimensional tradeoff space, and understanding the space is the prerequisite for navigating it.