The Kubernetes scheduler plays a crucial role in allocating resources to pods in a cluster. However, if not properly sized, containers may end up using more resources than needed, leading to increased cloud bills and potentially even out-of-memory conditions. To mitigate this issue, FinOps professionals can follow practical tips such as estimating resource requirements based on application code and benchmarking, optimizing resource allocation with tools like the Vertical Pod Autoscaler, and utilizing historical data from Datadog to rightsize workloads. Additionally, setting limits for CPU and memory requests can help ensure better predictability and avoid wasting resources. Ultimately, it is essential to take into account the nature of your workloads and business needs when setting resource requests and limits.