Effective pod scheduling is crucial for Kubernetes infrastructure performance, resilience, and cost efficiency. According to the 2025 Kubernetes Benchmark Report, the average CPU utilization is just 10% and memory utilization is 23% across Kubernetes clusters. Improper pod distribution can lead to decreased resilience beyond resource efficiency. The three powerful pod scheduling mechanisms in Kubernetes are pod affinity, pod anti-affinity, and topology spread constraints, each serving distinct purposes while sharing some overlapping functionality. Understanding their nuances, implementation patterns, and trade-offs is essential for designing high-performance, resilient, and cost-efficient Kubernetes architectures. Pod affinity attracts pods to nodes where other pods with specific labels are running, while pod anti-affinity repels pods from nodes where other pods with specific labels are running. Topology spread constraints distribute pods evenly across topology domains based on configurable parameters. Pod affinity is useful for co-locating related services to minimize latency or maximize resource sharing, but can be used inappropriately for large-scale deployments or resilience-critical applications. Pod anti-affinity prevents pods from co-locating with specific other pods, ensuring service availability during infrastructure disruptions. Topology spread constraints represent Kubernetes' most sophisticated pod distribution mechanism, enabling fine-grained control over pod distribution ratios across topology domains. By mastering these scheduling mechanisms, organizations can build resilient, efficient, and cost-effective infrastructure that maintains availability through infrastructure disruptions while optimizing resource utilization. The key takeaway is to use the right tool for the job: pod affinity for co-location and performance optimization, pod anti-affinity for basic separation and critical service resilience, and topology spread constraints for flexible, multi-level distribution at scale.