Public cloud providers offer hundreds of heterogeneous hardware instances. For analytical query processing systems, this presents both a challenge and an opportunity. Depending on the hardware configuration, performance and cost may differ by orders of magnitude. In this talk, I will present a simple and intuitive model that takes the workload, hardware, and cost into account to determine the optimal instance configuration. Such a model-based approach can significantly reduce costs and also guide the evolution of cloud-native database systems to achieve cost-optimal query processing in the cloud.