Why a single performance number tells only part of the story in LLM serving, and when you need the whole Pareto frontier instead. Covers the limits of scalarization on concave trade-offs, how Optuna and Google Vizier actually store trial values (the frontier is derived, not persisted), and how production tools like Prism, InferenceBench, and NVIDIA Dynamo present the frontier as the reportable artifact.