Pod Rightsizing
Continuously match CPU and RAM to real-time demand, driving down container costs while freeing your Ops team from tedious manual tuning.
The Problem
scaling falls short
As demand constantly shifts, keeping CPU and RAM requests accurate requires nonstop manual tuning, hard to keep up with and even harder to get right. Some teams turn to Kubernetes-native VPA to prevent throttling, OOM crashes, and wasted spend. But it has limits and often clashes with HPA, making it a poor fit for most.
Product Capabilities
with accurate scaling
CPU and RAM
rightsizing
Automatically allocate the precise amount of CPU and RAM needed to eliminate waste and ensure peak performance.
Precise container-level optimization
Ensure each container in a multi-container pod—sidecars, adapters, or helpers—gets the exact CPU and RAM needed for optimal performance.
Compatibility
with HPA
Boost performance and cost efficiency with VPA working alongside HPA for seamless resource optimization.
Workload-level
Insights
Gain real-time insights over your workloads’ utilization, costs, and savings opportunities, to reduce CPU & RAM overprovisioning and optimize allocations.
How It Works
Pod Rightsizing
Our solution continuously monitors pod usage and applies advanced algorithms to adjust workload resources. Designed with performance in mind, it lets you tailor scaling to your buffer needs. An auto-healing mechanism ensures stability, while gradual rollouts minimize the impact of scaling, all without requiring pod restarts. With Zesty, CPU and RAM optimization is more accurate than ever.
Benefits
Cut CPU &
RAM costs
Allocate just the right amount of CPU and RAM resources needed to boost cost efficiency and reduce waste.
Enhance app
performance
Optimize resource distribution across containers within a pod to mitigate CPU throttling and OOM cases.
Eliminate
manual tasks
Save time on usage monitoring and resource adjustments with real-time automation, not just recommendations.
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.
