In-depth analysis of thousands of active clusters – with almost a million CPUs under management – provides key insights into the current state of cloud cost optimization and energy consumption.

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CAST AI, the AI-driven cloud management company which offers a full Kubernetes automation and cost optimization platform, today published ‘The State of Kubernetes Report: Overprovisioning in Real-Life Containerized Applications.’ Key findings were based on an in-depth analysis of thousands of clusters running cloud-based applications.

The report offers new insight into overprovisioning of resources, overspending on cloud costs, and the resulting waste in energy consumption. Clusters were selected for analysis based on the highest activity level, running applications on the three most popular cloud services globally: Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP).

Key findings include:

  • On average, companies provision 1/3 more cloud resources than they end up using, resulting in substantial monetary and environmental waste.
  • By eliminating overprovisioning, companies can reduce their cloud spend by almost half.
  • By utilizing spot instances, organizations can reduce their cloud spend by 60 percent on average.
  • Organizations can further minimize their environmental impact by selecting newer machine types with less power-hungry CPUs, reducing their cloud bills and overall energy footprints.

Overprovisioning has regional and global environmental implications. According to, a 30 percent decrease in data center power consumption is enough to power the entire U.S. for almost 40 years. And some data centers rely on less sustainable power sources, such as coal or natural gas.

“Our new in-depth cluster analysis clearly shows the financial and environmental burden organizations are under as a result of continued overprovisioning,”  said Laurent Gil, co-founder and chief product officer, CAST AI. “We do see a trend where organizations are moving away from long term commitment pricing models to instead leverage real time rightsizing and spot instances, resulting in substantial savings. CAST AI is purpose-built to help with this challenge.”

CAST AI’s free cluster analysis tool is utilized by organizations around the world to determine how their cloud resources are provisioned. Customers can then use CAST AI’s advanced algorithms to make rightsizing instant, continuous, and as easy as clicking a button.

“Picking the most cost effective, energy efficient processors – while provisioning cloud resources optimally over time as your application demand fluctuates – is an incredibly complex problem to address,” said Yuri Frayman, founder and CEO, CAST AI. “With CAST AI, organizations are able to ‘set it and forget it’, taking advantage of the platform’s advanced AI driven algorithms to continuously determine the optimal cost/performance ratio while ensuring high availability to satisfy SLAs.”

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.

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