Intel Granulate announced the release of the Auto-Pilot functionality for recommendation implementation in its Kubernetes Optimization solution. This feature allows Kubernetes users to opt into autonomous optimization, which automatically and continuously adapts resource requests and HPA settings in real-time to reduce CPU and memory overhead, savings of up to 45% while adhering to the user's performance requirements.

This is a significant advancement for all Kubernetes users, whether self-managed, EKS, AKS, GKE, OpenShift or even a federated cluster, with DevOps professionals standing to benefit particularly. The Auto-Pilot functionality not only optimizes on the Kubernetes layer, but also integrates seamlessly with Intel Granulate's unique app-level optimization capabilities. This dual-level optimization achieves enhanced performance and lower costs compared to competitor offerings that are hyper-focused on Kubernetes rightsizing or bin-packing.

"With this added auto-pilot capability to Intel Granulate's capacity optimization, we are able to offer a holistic solution that empowers Kubernetes users to reduce overprovisioning while avoiding higher latency and throttling," said Asaf Ezra, CEO at Intel Granulate.

Intel Granulate's Kubernetes Optimization solution provides several unique benefits for engineers managing orchestrated applications:

• Effortlessly Eliminate over-provisioning: Let the Auto-Pilot automatically rightsize your workloads and pay only for what you use
• Optimize across the cloud stack: Gain holistic, multi-level performance improvements by combining autonomous runtime optimization and Kubernetes rightsizing
• Gain full customization and visibility of your clusters: Easily configure your capacity optimization to your application's needs, whether per cluster or label, to discover CPU, memory and cost reduction opportunities
• Ensure optimal performance: Keep your competitive SLAs while reducing your Kubernetes costs without compromising resiliency, availability or stability.