The ‘State of Kubernetes Report: Overprovisioning in Real-Life Containerized Applications’ was released by CAST AI, a firm that provides a complete Kubernetes automation and cost optimization platform. Key conclusions came from a thorough examination of thousands of clusters that were running cloud-based applications.
The paper would provide fresh information on resource overprovisioning, excessive cloud expense, and the ensuing energy waste. Based on the greatest degree of activity, clusters running the three most widely used cloud services worldwide – Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform – were chosen for investigation (GCP).
Key findings of this analysis by CAST AI would include the following:
- Companies would typically supply a third more cloud resources than they actually need, resulting in significant financial and environmental waste
- Overprovisioning may be eliminated, and businesses can cut their cloud spending in half
- Spot instances would enable businesses to cut their cloud spending by, on average, 60 percent
- By choosing more recent machine types with fewer power-hungry CPUs, businesses may further lessen their environmental effect while also lowering their cloud costs and total energy footprints
Free Cluster Analysis Tool
Regional and global environmental effects would result from overprovisioning. According to Worlddata.info, a 30 percent reduction in data center power use would provide enough energy to run the U.S. as a whole for close to 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 of 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.”
Organizations all across the world use CAST AI’s free cluster analysis tool to find out how their cloud resources are allocated. Then, customers may utilize the sophisticated algorithms of CAST AI to “instantly, continuously, and simply” by clicking a button do rightsizing.
“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 of 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
According to the company itself, customers of AWS, Google Cloud Platform (GCP), and Microsoft Azure may reduce their cloud costs in half thanks to cloud optimization platform CAST AI. It uses AI to assess several data points and determine the best cost-performance ratio. For all sorts of Kubernetes workloads, the platform would offer a high-performing and robust infrastructure. CAST AI is based in Miami, Florida, and has a branch in Vilnius, Lithuania in Europe.
