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The performance of software systems remains a persistent concern in the field of software engineering. While traditional metrics like binary size and execution time have long been focal points for developers, the power consumption concern has gained significant attention, adding a layer of complexity to performance evaluation. Configurable software systems, with their potential for numerous configurations, further complicate this evaluation process. In this experience paper, we examine the impact of configurations on performance, specifically focusing on the web stack generator JHipster. Our goal is to understand how configuration choices within JHipster influence the performance of the generated system. We undertake an exhaustive analysis of JHipster by examining its configurations and their effects on system performance. Additionally, we explore individual configuration options to gauge their specific influence on performance. Through this process, we develop a comprehensive performance model for JHipster, enabling us to automate the identification of configurations that optimize specific performance metrics. In particular, we identify configurations that demonstrate near-optimal performance across multiple indicators and report on significant correlations between configuration choices within JHipster and the performance of generated systems.