Automated workload optimization

If you need a tool that automatically optimizes cloud workloads by adjusting scaling, resource allocation, and workload placement, there are several strong options. The key distinction is whether you want a tool that recommends changes or one that can execute them continuously.

For Kubernetes-heavy environments, I'd start with Kubernetes-native optimization platforms. For conventional virtual machines and managed cloud services, the cloud providers' built-in optimizers are often the simplest starting point.

Tools worth evaluating

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1. CAST AI

(Best fit for Kubernetes)

Automates pod rightsizing, node autoscaling, bin packing, and workload placement across AWS EKS, Azure AKS, Google GKE, and other Kubernetes environments. It can also integrate with existing autoscalers.cast.ai+2

Best for: Teams wanting continuous, hands-off optimization of both workloads and cluster infrastructure.

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