StormForge, a cloud-native application performance testing and resource optimization company, has announced that its StormForge Optimize Live solution now enables the bi-dimensional Kubernetes pod autoscaling. The enhanced capabilities are available now.

StormForge Optimize Live uses machine learning to automatically right-size pods while also setting a desired target utilization for the horizontal pod autoscaler (HPA). This enables vertical and horizontal autoscaling to work together without contention, to minimize resource usage and cost without sacrificing application performance or reliability.

Autoscaling is both a significant challenge and opportunity for Kubernetes users. Kubernetes natively offers two primary ways of dynamically scaling applications – the HPA and the vertical pod autoscaler (VPA) – but it’s not possible to use both together without extensive customization effort. Additionally, the HPA requires users to set a target utilization that determines when to add or remove replicas, but it’s nearly impossible to arrive at an optimal target utilization using manual methods.

With the new release of StormForge Optimize Live, organizations are no longer limited to using just horizontal scaling without proper tuning, leaving substantial cloud resource savings untapped.

By enabling bi-dimensional Kubernetes pod autoscaling, StormForge Optimize Live ensures continuous right-sizing of Kubernetes applications; reduces Kubernetes application resource usage and costs while still ensuring performance and reliability; and reduces the risk of out-of-memory (OOM) errors and CPU throttling while minimizing cloud costs.

Click here to sign up for a free trial of StormForge Optimize Live.