Headroom Reduction
to speed up app boot time
Make application start time 5X faster and confidently reduce the CPU overprovisioning you keep to handle traffic spikes.
The Problem
Cluster Autoscaler is a powerful tool for scaling new nodes, but when traffic spikes occur, it often takes a few minutes for Cluster Autoscaler to launch a new node and for the freshly created pod to be ready to serve requests.
The typical workaround is to overprovision pods and nodes—but that comes at a steep cost: low CPU utilization, wasted resources, and inflated cloud bills.
Product Capabilities
with precise, instant scaling
Large-scale node
hibernation
Automatically create a pool of hibernated nodes at a fraction of the cost, ensuring you respond faster to any sudden surge in traffic.
5X faster node
scale-out
With pre-warmed hibernated nodes deployed in just 30 seconds, reduce application boot time to better handle traffic spikes and ensure stability.
Idle pod
reduction
Safely remove idle pod replicas to cut node overprovisioning, optimize CPU utilization, and drive down costs.
Workload level
Insights
Gain real-time visibility & insights over your workloads’ utilization, costs, and savings opportunities, to reduce CPU overprovisioning and boost cost efficiency.
How It Works
By integrating Cluster Autoscaler’s node scaling with Zesty’s trademarked HiberScale pre-warming technology—caching container images and pre-starting OS services—we enable faster, more precise scaling than ever before.
Benefits
Cut CPU costs
Reduce CPU buffer by up to 70% and stop paying for resources you don’t use just to maintain SLAs.
Ensure app availability
Handle any traffic peak with speed and precision, ensuring your application stays reliable no matter the demand.
Eliminate manual operations
Cut wasted hours of manual prediction, configuration, and monitoring with an automation stack you can trust.
Integrations
Whether you’re managing dynamic workloads
or scaling clusters, Zesty ensures seamless integration across your Kubernetes infrastructure to reduce overprovisioning and boost efficiency.
Interested to learn more?
Download the solution brief
If you’ve made it this far, these questions are for you
How does the pricing model work?
Our pricing model is designed to be straightforward and transparent. We charge a base fee plus a fee per CPU managed by Zesty. Importantly, you’re only billed for the CPU managed after optimization. This ensures that you pay only for the resources we actively manage, delivering clear value with every CPU optimized.
Which autoscalers are supported?
Headroom Reduction supports both Cluster Autoscaler (CAS) and Karpenter, enabling headroom reduction across a wide range of Kubernetes environments.
Is the platform secure?
Does it require an agent in order to work?
Will cost optimization impact my applications’ performance?
No, our platform is designed to maintain performance, ensure stability and preserve SLAs, while optimizing costs. Automation keeps CPU available when needed, ensuring applications run smoothly even as costs are reduced.
Is there a complex setup or onboarding process?
How long does it take to see savings after implementation?
Recommendations are available about seven days after connecting a cluster to Kompass. Once a recommendation is activated, headroom reduction is fully automated. Users start seeing measurable savings as early as one hour after activation.
