Headroom Reduction
Faster boot time, zero waste.
Handle unpredictable usage spikes smoothly, keep CPU buffers lean, and maintain high availability without resource waste.
The Problem
Even with Karpenter, slow boot times can hurt performance. The usual workaround is to overprovision replicas, but that leads to low CPU utilization, wasted resources, and inflated cloud costs.
This is exactly what we’re here to solve.
Product Capabilities
with precise, faster scaling
Large-scale node
hibernation
Automatically create a pool of hibernated nodes at a fraction of the cost, ready to handle any traffic spike.
5X faster app
boot time
Reduce application boot time to ensure faster response to traffic spikes and greater stability.
Idle pod
reduction
Safely remove idle pod replicas to cut node overprovisioning, optimize CPU utilization, and drive down costs.
Precise prediction
models
Leverage advanced algorithms to analyze historical and real-time utilization patterns, forecast workload demand, and proactively adjust resources.
Workload level
Insights
Gain real-time insights over your workloads’ utilization, costs, and savings opportunities, to reduce CPU overprovisioning and boost cost efficiency.
How It Works
FastScaler™ works alongside Karpenter and Cluster Autoscaler to accelerate application startup time, achieving scale-out up to 5x faster.
By combining hibernation and node pre-warming-caching container images, and pre-starting OS services, it enables horizontal scaling decisions to execute instantly and precisely when demand spikes.
Benefits
Cut CPU costs
Reduce CPU buffer by up to 70% and stop paying for resources you don’t use, kept 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
Ready to dive deeper?
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?
Zesty requires an agent with read-only permissions to gain visibility into your environment and provide accurate recommendations. For our automated headroom reduction solution, an additional agent is needed to enhance efficient automation, requiring permissions for creating nodes, reading logs from Cloudwatch, events from SQS, and more.
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.
