Stackwatch Adds Spot Pricing Analytics to Kubecost Tool
Stackwatch has added spot pricing analytics to its open source Kubecost tool for Kubernetes cluster monitoring and management. The analytics features enable IT teams to identify which clusters are best suited to run on the less costly spot instances that cloud service providers make available.
Kubecost Spot Commander collects data via the Kubernetes application programming interface (API) to apply heuristics against a spot checklist. Kubecost Spot Commander then identifies classes of workloads that could run on spot instances that cloud service providers make available for a limited amount of time at a deep discount.
This extension to the Kubecost tool can be applied to spot instances of cloud infrastructure made available by Amazon Web Services (AWS), Microsoft and Google. Kubercost Spot Commander automatically updates the cluster configuration to include spot nodes as they are made available by a cloud service provider which enables an IT team to reduce the number of on-demand nodes they need to employ.
Stackwatch CEO Webb Brown notes that as much as 80% of the spending on cloud infrastructure is wasted because many workloads only run intermittently. Kubecost Spot Commander analyzes workload usage patterns, node capacities and pricing data made available by the cloud service provider to identify the least expensive type of nodes available to run those workloads, says Brown.
The primary issue organizations encounter in reining in cloud costs is the lack of visibility into the cloud environment, adds Brown. Kubecost was created to provide the insight required to optimally run workloads on Kubernetes clusters that are designed to dynamically scale up and down as workload requirements change, notes Brown.
At the start of 2022, it’s apparent the cost of deploying and running fleets of Kubernetes clusters will become a much more pressing issue for IT organizations. Naturally, a large percentage of those workloads will be deployed on cloud services. Tools such as Kubecost make it easier for IT teams to determine which cloud service provider offers the most cost-effective way to run those workloads.
Of course, many enterprise IT organizations will have signed contracts with cloud service providers that guarantee discounted pricing if they run a certain number of workloads per month. However, many IT organizations prefer to continuously monitor pricing offered by multiple cloud service providers to reduce their overall costs. Regardless of approach, interest in reining in costs is on the rise as the percentage of workloads running on cloud platforms continues to steadily increase. Many of those workloads are now running on Kubernetes clusters as organizations rely on containers to build and deploy microservices-based applications.
Going forward, it’s not clear the degree to which IT teams will centralize the management of Kubernetes clusters in 2022. However, as the percentage of workloads running on Kubernetes clusters steadily increases, the more likely it becomes those platforms will be centrally managed by an IT operations team that must optimize usage of cloud infrastructure. The first immediate challenge, of course, is determining what classes of workloads are actually running on which cloud service.
