Multi-dimensional autoscaling
Automatically align pod-level resource requests and HPA minReplicas with real workload behavior to maximize cluster utilization, cut cloud costs, and maintain performance under load.
Product Capabilities
multi-dimensional autoscaling
Pod
Rightsizing
Continuously tune pod CPU and memory based on real workload usage. Eliminate overprovisioning and keep clusters efficient without throttling or OOM kills.
Min Replicas
optimization
Continuously tune minReplicas to match real workload demand. Prevent excess capacity while ensuring applications maintain the replicas needed for stability.
HPA & VPA
coordination
Optimize replica resource requests and replica counts together, while integrating with native HPA and Keda to ensure accurate optimization without conflicts.
Kubernetes workload
compatibility
Optimize a wide range of Kubernetes workloads, including Deployments, StatefulSets, Jobs, Java, custom workload types, and more. Adapt scaling and resource allocation to different workload patterns across the cluster.
Policy-Driven
Automation
Define guardrails that control how optimization is applied across workloads. Align scaling behavior with performance and cost goals and set safety margins to meet your exact preferences.
Built-in safety
mechanisms
Update pod resource allocations without restarts, while gradual rollouts and auto-healing mechanism preserve stability during scaling events.
Comparison
VP of Engineering at Wildflower Health
Platform Engineering Lead at Sennder
Benefits
Reduce
compute costs
Allocate just the right amount of CPU, RAM, and replicas needed to boost cluster efficiency and reduce resource waste.
Enhance
app performance
Mitigate throttling and OOM cases under load with real-time optimization of replica counts and pod resource distribution.
Eliminate
manual tuning
Free your engineers from forecasting, monitoring, and adjusting resources manually with real-time automation.
installation
Fast deployment.
Full control.
Get started in minutes with a simple setup, clear recommendations, and full control over how optimization is applied.
Integrations
Works with HPA, KEDA, and any node autoscaler, as well as Git tools and common observability stacks. No sidecars. No app code changes. No new observability stack required.
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.
