The text discusses the challenges and strategies involved in managing Kubernetes workloads, focusing particularly on the issues of overprovisioning and the importance of autoscaling to optimize cloud costs. It introduces Datadog’s Watermark Pod Autoscaler (WPA) as an enhancement to the traditional Horizontal Pod Autoscaler (HPA), offering more flexibility with scaling decisions through high and low watermarks. The text highlights the significance of proper vertical and horizontal provisioning before implementing autoscaling to achieve high resource utilization and cost savings. It emphasizes the necessity of choosing the right metrics for scaling, such as using request queue length over CPU utilization for more proactive scaling decisions. The article underscores that successful autoscaling requires continuous monitoring and tuning of parameters like scaling velocity and cooldown periods to maintain a balance between performance and cost efficiency. Finally, it suggests that autoscaling is not a "set it and forget it" solution, necessitating regular adjustments to adapt to evolving workloads and traffic patterns, and promotes Datadog's fully managed Kubernetes Autoscaling service for simplifying scaling strategies.