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CAST AI announces the launch of AWS EKS Optimizer to help companies cut Kubernetes cloud costs

CAST AI announced the launch of a brand-new product dedicated to companies that run Kubernetes on AWS Elastic Kubernetes Service (EKS).

Last year, CAST AI received $8 million of VC funding to scale its cloud optimization platform. The platform streamlines performance and optimizes the costs of running clusters in AWS, Azure, Google Cloud Platform, and DigitalOcean. Early users of CAST AI slashed their cloud bills by 52-84 percent. With some of CAST's advanced setup features, savings have reached as much as 92 percent.

Now, CAST AI launched the first product to help teams optimize any Amazon Elastic Kubernetes Service (EKS) Kubernetes environment. Azure Kubernetes Service (AKS) and Google Kubernetes Engine (GKE) optimization solutions are already on the roadmap and coming in a few months.

"A large number of companies have expressed interest in EKS optimization. Many organizations are urgently looking for ways to optimize their cloud bill. We have already signed contracts within industries such as e-commerce, data analytics, data science, cybersecurity, and SaaS application delivery," says Laurent Gil, Chief Product Officer and co-founder of CAST AI.

The EKS Optimizer is now available directly within the CAST AI platform. Following excellent feedback from partners and customers, the solution should also become available on the AWS Marketplace in the near future.

"After trying CAST AI, we wouldn't do cloud cost optimization any other way. The automated approach of CAST AI guarantees the ideal infrastructure setup at all times and saves engineers hours of work," says Patrick Booher, Vice President of Customer Success at Zazmic Inc, a partner of CAST AI.

By connecting existing EKS clusters in a read-only mode, users get instant savings reports and can identify opportunities for cutting costs. To proceed with full automation, users can set optimization policies and leave AI in charge of optimizing their cloud bill.

"We built the EKS Optimizer, so teams no longer need to invest time and effort into monitoring, analyzing, and optimizing their Kubernetes environments on AWS. Our platform handles the provisioning, de-provisioning, and autoscaling of workloads for our customers, all the while negotiating the best possible resources available on AWS on their behalf in real time," says Leon Kuperman, CTO and co-founder of CAST AI.

Today, the majority of Kubernetes environments underutilize the resources assigned to them. CAST AI's granular approach to autoscaling addresses resource requirements precisely, taking into account the actual work to be performed and required scale rather than following a predetermined DevOps formula. For monolithic, Docker-based applications, the platform achieves cost savings of around 50 percent, while for applications based on microservices, the platform obtains closer to 75-80 percent of cost reduction.

"The EKS Optimizer finally opens the doors to using EC2 spot instances that bring 80-90 percent of cost savings when compared to on-demand pricing. Since spot instances can be interrupted at any moment, many companies refrain from using them. CAST AI solves this problem through several approaches, including burst scaling of pods when required. If AWS pulls the plug on an instance, the CAST AI platform anticipates the interruption and takes measures to ensure that there is always an adequate capacity to avoid service disruption," adds Kuperman.

"Cloud cost optimization is an essential practice for every company that wants to use containerization effectively," says Robert Duncan, CISO of DirectLine UK.

Thanks to a unique blend of automation and optimization algorithms, CAST AI has opened the next chapter in DevOps automation, empowering teams to reap the full benefits of their cloud investments without any extra work.

CAST AI will show how the new solution achieves the incredibly high cost savings in the company booth and demos held by its CTO during KubeCon Europe 2021.

Published Monday, May 03, 2021 8:01 AM by David Marshall
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