In a Kubernetes environment, hidden challenges during pod initialization can lead to startup failures and performance issues, as illustrated by a scenario involving over 100 workloads on a single node. Despite having adequate resources based on pod requests, these workloads faced intermittent startup failures due to two main problems: CPU spikes during initialization, particularly for Java applications, and large Docker image sizes causing extended image pull times and high disk I/O. These challenges were addressed through a comprehensive optimization approach that included configuring startup probes, removing CPU limits to allow burst usage, using priority classes for smarter scheduling, and implementing image optimization techniques. The result was a significant improvement in pod startup success rates, a 40% reduction in Java application initialization times, and a 90% decrease in image pull failures, all without increasing the infrastructure footprint. This case highlights the importance of understanding application behavior beyond their steady state and configuring Kubernetes to align with specific workload patterns for enhanced reliability and efficiency.