Kubernetes serves as the core of modern cloud-native infrastructure, supporting a wide range of applications from simple web apps to sophisticated machine learning pipelines. As organizations expand their Kubernetes clusters and diversify their workloads, they face challenges in selecting the appropriate workload types and optimizing resources to prevent unnecessary expenditure. The choice between stateful and stateless applications is crucial, with Deployments being suitable for most stateless applications and StatefulSets necessary for applications requiring persistent storage. Other workload types include DaemonSets for system-level services, Jobs for one-time tasks, and CronJobs for scheduled tasks. Custom Resources cater to complex applications with specialized needs. Best practices in workload selection emphasize understanding application characteristics, operational complexity, and future growth to ensure performance, efficiency, and cost-effectiveness. Matching application requirements with the correct Kubernetes workload controller is essential for reducing operational overhead and optimizing costs, with a deeper exploration of overprovisioning patterns to follow in subsequent discussions.