Kubecost Spot Commander Adapts Kubernetes Clusters for Spot Nodes Across AWS, GCP, and Azure

Kubecost Spot Commander Adapts Kubernetes Clusters for Spot Nodes Across AWS, GCP, and Azure
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Kubecost, a tool delivering Kubernetes cost monitoring and management at scale, today announced the release of Kubecost Spot Commander.

The new functionality identifies workloads that are both safe and ready to run on spot instances, delivering significant savings by dynamically optimizing Kubernetes cluster configurations.

Kubecost supports organizations currently using – or looking to take advantage of – spot or preemptible nodes in Kubernetes clusters running across AWS, GCP, or Azure. Spot Commander determines which workloads are safe to run on spot and selects a new set of nodes to handle the split of spot-ready and non-spot-ready workloads.

"Cloud providers offer spot and preemptible nodes at up to a 90 percent discount by utilizing their excess compute capacity," said Webb Brown, CEO and co-founder of Kubecost. "The challenge comes with ensuring you can proactively take advantage of this capacity (and realize those reduced operating costs) without hindering workload performance or availability. Kubecost Spot Commander is built to enable Kubernetes domain experts to more efficiently consider which workloads can be optimized as spot and preemptible nodes. We believe that the Kubecost recommendations delivered through Spot Commander, which include estimated savings, will motivate more infrastructure teams to start realizing significant cost reductions."

Kubecost Spot Commander works by:

  • Analyzing cluster workloads for spot-readiness: Spot Commander analyzes the workloads running on a cluster to determine spot-readiness using Kubecost's Spot Checklist feature. The tool harnesses information from the Kubernetes API and heuristics to pinpoint workloads that are ready.

  • Suggesting optimal cluster configuration: Once spot-ready workloads have been identified, Kubecost leverages its extensive knowledge (see what information is available via its Allocation API on GitHub) of an organization's workload usage patterns and the node capacities and pricing of their cloud provider. With this data, Spot Commander intelligently suggests the least expensive node types and quantities to support spot-ready and non-spot-ready workloads. The tool can also calculate overall savings for adopting the new cluster configuration by calculating the current run-rate of a cluster's nodes and comparing it with the approximate run-rate of the new configuration.

  • Adopting the recommended configuration: To realize the estimated savings, Spot Commander updates the cluster configuration to include new spot nodes and reduce the number of on-demand nodes. Doing so ensures that only spot-ready workloads are scheduled on spot nodes.

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