Overprovisioning results in significant cost with no material benefit whatsoever.

CAST AI, the AI-driven cloud optimization company offering a full Kubernetes automation and cost optimization platform, analyzed infrastructure utilization reports for more than 400 organizations to quantify how much they overspend on cloud costs. These organizations included large enterprises running applications across tens of thousands of CPUs, down to small companies running their applications on just a few nodes. CAST AI’s cluster analysis tool is free to use, and provides detailed insight into how a company’s cloud resources are provisioned, as well as specific opportunities for optimization and cost savings.

“We performed a detailed analysis for hundreds of organizations running their applications in the cloud,” said Laurent Gil, co-founder and chief product officer, CAST AI. “Our advanced AI engine provides full visibility into how much you’re currently paying for cloud resources, as well as how much you would save if those resources were optimized.”

“CAST AI will then instantly rebalance and optimize your cluster configuration in perpetuity, while hitting the cost savings target within just a few minutes,” continued Yuri Frayman, chief executive officer, CAST AI. “All of this is directly reflected in the bill you get from your cloud provider. After aggregating anonymous data, we have unparalleled insight into exactly how much companies overspend. Between 50 to 75 percent on average.”

CAST AI’s analysis surfaced a number of key findings, both in terms of overprovisioning and how much companies overspend as a direct result. These include: 

  • On average, organizations spend 3x more than they should on cloud costs.
  • 98 percent of organizations had substantial cost savings.
  • The main driver for this is the over-provisioning of expensive resources, resulting in significant cost with no material benefit whatsoever.  
  • Almost 2/3 of the cost waste is the result of CPUs and memory that are provisioned but not utilized, combined with the selection of cost inefficient VMs with expensive CPUs and too large memory ratio.
  • The remaining waste comes from under leveraging the use of spot instances for containers that are qualified to be spot friendly.

Provisioning remains a significant challenge for organizations of all sizes. Cloud providers typically have more than 600 different instance types to choose from, so even the most experienced and technically adept DevOps and SRE professionals benefit considerably from automation; it is difficult and time consuming to select the right types and quantity of VMs while making sure that the infrastructure is continuously rightsized.

With new advances in AI it is now easy to make this process both instant and automated, so that requested, provisioned and utilized CPUs are 100 percent in sync, and remain in sync over time. This is what CAST AI’s advanced platform was specifically designed to address, resulting in significant cost savings for customers while providing unique insights into cloud overprovisioning and overspend.

About CAST AI:

CAST AI is the leading Kubernetes automation platform for AWS, Azure, and GCP customers. CAST AI goes beyond monitoring clusters and making recommendations; the platform utilizes advanced machine learning algorithms to analyze and automatically optimize clusters in real time, saving customers 50% or more on their cloud costs, and improving performance and reliability to bolster DevOps and engineering productivity.

Learn more: https://cast.ai/

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