Automated Cost Optimization for Kubernetes Workloads

•Use Case: Optimize the cost of Kubernetes workloads by automating the scaling of pods and nodes based on demand.

•The platform can ensure that resources are provisioned efficiently to meet demand without overprovisioning, thereby optimizing costs.

• Integrating with Kubernetes HPA for automatic pod scaling based on real-time metrics.

• Managing and automating the deployment of Cluster Autoscaler to scale the underlying EC2 nodes.

• Monitoring and optimizing resource allocation using VPA (Vertical Pod Autoscaler).

• Implementing scaling policies and automation workflows to ensure cost-efficient and responsive scaling operations.

• Enabling auto-scaling for serverless workloads with Fargate profiles, reducing the need for manual scaling interventions.