Transitioning from static servers to Kubernetes offered the promise of automatic scalability, which contrasted sharply with the manual interventions previously required to meet customer demands. Despite its potential, early adoption of Kubernetes presented several challenges, including high costs from unoptimized deployments, a steep learning curve, and the complexities of optimizing Horizontal Pod Autoscaling (HPA) for stability. The process of scaling down was less efficient, leading to resource wastage and higher expenses, while understanding Kubernetes' unique language and resource management strategies proved demanding. Mastering Kubernetes involved learning to set appropriate resource 'requests' and 'limits' to ensure efficient workload distribution and avoid unnecessary costs. Although the journey was arduous, it led to a deeper understanding of Kubernetes' operational capabilities and opened opportunities for future optimization. The team remains committed to overcoming these challenges and harnessing the full potential of Kubernetes for improved scalability and cost efficiency.