Modern software-intensive systems must often guarantee certain quality requirements under changing run-time conditions and high levels of uncertainty. Self-adaptation has proven to be an effective way to engineer systems that can address such challenges, but many of these approaches are purely reactive and adapt only after a failure has taken place. To overcome some of the limitations of reactive approaches (e.g., lagging behind environment changes and favoring short-term improvements), recent proactive self-adaptation mechanisms apply ideas from control theory, such as model predictive control (MPC), to improve adaptation. When selecting which MPC approach to apply, the improvement that can be obtained with each approach is scenario-dependent, and so guidance is needed to better understand how to choose an approach for a given situation. In this paper, we compare CobRA and PLA, two approaches that are inspired by MPC. CobRA is a requirements-based approach that applies control theory, whereas PLA is architecture-based and applies stochastic analysis. We compare the two approaches applied to RUBiS, a benchmark system for web and cloud application performance, discussing the required expertise needed to use both approaches and comparing their run-time performance with respect to different metrics.