This chapter explores the differences between reactive and proactive autoscaling approaches in Kubernetes and their role in optimizing cloud-native performance. It begins with an overview of Kubernetes autoscaling mechanisms and categories, contrasting reactive strategies, which respond to real-time workload fluctuations, with proactive strategies, which forecast future demand using predictive models. The integration of operator reconciliation with model-driven control systems is highlighted as a method for refining resource allocation. Performance evaluation is presented through real-world implementations, including Kubernetes–Docker environments and a case study on dynamic allocation in cloud computing. Key findings demonstrate the benefits of predictive autoscaling, including reduced latency, optimized cloud expenses, and improved response times across diverse workloads. The chapter concludes with a forward-looking perspective on advancing predictive autoscaling techniques for both cloud and edge computing, emphasizing the importance of intelligent, demand-aware orchestration in meeting evolving performance and cost requirements.