Kubernetes Cost Optimization with StormForge and Karpenter

Presented by

Erwin Daria, Director, Technical Alliances, StormForge | Sai Kotagiri, Partner Solutions Architect, AWS

About this talk

Cloud infrastructure costs are continuing to rise, reaching unsustainable levels for many organizations. At the same time, teams are being asked to do more with less, making cloud cost optimization a high priority for many organizations in 2023. For teams running cloud native applications on Kubernetes however, ensuring efficient use of resources is extremely difficult. Teams have to guess at the best resource settings, and then struggle to effectively manage resources at scale. In this webinar, we’ll demonstrate how to leverage intelligent tools like StormForge and Karpenter to quickly and easily optimize both applications and the infrastructure, providing a double-whammy that can reduce Kubernetes costs by up to 70% while also minimizing the operational overhead for your technical teams. Tune in to learn about: - The Kubernetes cost optimization conundrum, and why it’s so hard to solve. - How horizontal pod autoscaling, vertical pod autoscaling, and cluster autoscaling all must work together to achieve real cloud cost savings. - Avoiding the pitfalls of simplistic, unintelligent autoscaling approaches to achieve an automatic and continuous approach to optimization.

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Inefficient Kubernetes and application configurations result in millions of dollars in wasted cloud resources, business-impacting performance and availability issues, and thousands of hours of lost productivity every year. StormForge combines enterprise-grade performance testing with patent-pending machine learning to automatically find the optimal configurations for your applications, saving time and money while ensuring application performance and resiliency, allowing your organization to accelerate past your competition. StormForge is the only optimization solution purpose-built for Kubernetes that proactively ensures efficiency at scale by making intelligent business trade-offs between cost and performance without time-consuming, ineffective trial-and-error, and without needing to be a Kubernetes expert.