Kubernetes autoscaling guide: determine which solution is right for your use case

Kubernetes offers scalability solutions that enable organizations to maintain availability and high performance during traffic surges while reducing costs during lulls. However, scaling comes with tradeoffs and must be done carefully to avoid overprovisioning resources. There are various services available for workload and cluster scaling, each with different strengths and weaknesses suited for specific use cases. These include the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), Cluster Autoscaler (CA), Karpenter, and Datadog's Kubernetes Autoscaling and Watermark Pod Autoscaler (WPA). The choice of autoscaling solution depends on factors such as whether you are scaling an application or infrastructure, the nature of your workloads, and specific requirements for resource management and traffic handling.