Pod Rightsizing
built for performance
Run leaner, faster clusters with CPU and memory that adjust in real time, continuously tuning resources based on live workloads to keep performance high and costs under control.
The Problem
Kubernetes workloads are dynamic, with pod-level CPU and memory usage fluctuating as autoscalers respond to traffic and deployment changes.
Static resource requests and limits can’t adapt in real time, leading to over-provisioned nodes, throttled containers, and OOM kills. Teams waste time manually tuning instead of automating optimization.
Product Capabilities
Pod Rightsizing automation
Dynamic CPU and
Memory Rightsizing
Continuously adjust pod CPU and memory based on live utilization. Keep nodes efficient without throttling or OOM kills.
Container-level
optimization
Rightsize every container- sidecars, adapters, and helpers – to remove idle overhead and keep workloads balanced.
Native HPA & VPA
Compatibility
Run seamlessly with existing autoscalers. Zesty feeds accurate resource data back to HPA and VPA for smoother scaling.
In-Place
Pod Resizing
Use Kubernetes InPlacePodResizing to update resources without restarts. Maintain uptime during every rightsizing action.
Policy-Driven
Automation
Set rules per workload for buffers, safety margins, and risk tolerance. Choose between full automation or approval workflows.
Workload level
Insights
Gain real-time visibility & insights over your workloads’ utilization, costs, and savings opportunities, to reduce CPU & RAM overprovisioning and optimize allocations.
Comparison
VP of Engineering at Wildflower Health
How It Works
Zesty connects to your cluster, analyzes live metrics, and automatically tunes pod resources with zero disruption.
Observe: Monitor real-time CPU and memory to detect usage patterns.
Recommend: Identify optimal limits to sustain performance and reduce waste.
Act: Apply changes instantly or send recommendations for review.
Works seamlessly with EKS, AKS, and autoscalers like HPA and KEDA.
Benefits
Optimize CPU
& RAM utilization
Continuously allocate just the right CPU and memory per workload. Improve cluster utilization while preventing throttling or over-provisioning.
Boost application
stability
Balance resource distribution across containers to eliminate CPU contention and OOM events during peak load and scaling conditions
Eliminate
manual tuning
Skip usage monitoring and YAML edits. Zesty adjusts resources in real time, keeping performance consistent without manual intervention.
Installation
Deploy Pod Rightsizing in minutes via Helm Chart with no disruption, and start optimizing right away.
Integrations
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.
Is the platform secure?
Yes, security is a priority. The platform complies with industry standards, encrypts all data, and offers role-based access controls, ensuring only authorized users can access your Kubernetes cost data and settings. Only meta-data and usage metrics are collected, Zesty doesn’t have access to any data on the disk or the EC2 instance. These metrics are reported to an encrypted endpoint, and sent unidirectionally to Zesty’s backend. All of Zesty’s architecture is serverless meaning there are no servers or databases involved and all data collected resides within AWS.
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 Pod Rightsizing solution, an additional agent is needed to enhance efficient automation, requiring permissions to apply changes on resource requests and enforce these changes.
Will cost optimization impact my applications’ performance?
No, our platform is designed to maintain performance, ensure stability, and preserve SLAs, all while optimizing costs. Automation ensures CPU and RAM are available when needed, by monitoring events like OOM or throttling, and keeps applications run smoothly with no pod restarts, even as costs are reduced.
Is there a complex setup or onboarding process?
No, our platform is designed for a quick and simple onboarding process. Most customers are up and running within minutes, with full support to ensure a smooth start on our platform.
How long does it take to see savings after implementation?
Recommendations are available about 24 hours after connecting a cluster to Kompass. Once a recommendation is activated, Pod Rightsizing is fully automated. Users start seeing measurable savings as early as one hour after activation.
