Kubernetes pod scheduling plays a critical role in how applications perform and how much teams pay to run them. Balancing factors such as cost efficiency, resource availability, fault tolerance, and workload priorities is essential for maintaining resilience without overspending. Scheduling policies significantly impact resource utilization and costs, with optimized distributions improving CPU utilization by 35-47% and memory utilization by 28-39%. To achieve optimal resource efficiency with appropriate resilience, teams can use node-level soft anti-affinity for non-critical services, reserve strict constraints for Zone/Region level or critical services, and group related non-critical services together. Proper distribution policies form the foundation of resilience engineering in Kubernetes, and implementing constraints at multiple levels is crucial for comprehensive resilience. Designing cascading constraint patterns from strict to flexible can help balance cost and resilience, while considering common pitfalls such as overly strict anti-affinity, conflicting affinity rules, excessive node specialization, ignoring scaling implications, and forgetting about resource constraints can help avoid service disruptions and performance issues. By following best practices, teams can create high-performance, robust, and cost-effective Kubernetes infrastructures that join the ranks of top-performing environments.