Multi-dimensional autoscaling
and vertical autoscaling
Automatically align vertical and horizontal resources with real workload behavior to maximize cluster utilization, cut infrastructure costs, and maintain performance under load.
Trusted by the best to optimize at scale
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.
Replicas optimization
Continuously tune Replicas to match
real workload demand.
Prevent excess baseline capacity while ensuring applications always maintain the replicas needed for stability.
HPA and VPA coordination
Work with native HPA and VPA to optimize resource requests and replica counts together, preventing conflicting scaling behavior and performance instability.
Kubernetes workload compatibility
Optimize a wide range of Kubernetes workloads, including Deployments, StatefulSets, CronJobs, Java, Argo 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. Gradual rollouts and automatic recovery mechanisms preserve stability during scaling events.
Our customers say it best
“We get over 40% optimization in cluster size, without having to manage it ourselves.”
Platform Engineering Lead at Sennder
“It used to take several hours out of my week. Now it’s completely off my plate.”
VP of Engineering at Wildflower Health
Autoscaling built for
always-optimized clusters
Reduce compute costs
Allocate just the right amount of CPU, RAM, and replicas needed to boost clusters efficiency and reduce resource waste.
Enhance app performance
Mitigate throttling and OOM cases during scaling and peak load with real-time optimization of replica counts and pod resource distribution.
Eliminate manual tuning
If you’ve made it this far, these questions are for you
How does the pricing model work?
How do horizontal and vertical autoscaling work together without creating conflicts?
Multi-dimensional autoscaling coordinates both scaling mechanisms so they work together instead of competing. CPU and memory requests are continuously rightsized while replica counts scale based on demand. By keeping requests and replicas aligned with real-time usage, the system avoids scaling loops and maintains stable workload performance.
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 multi-dimensional autoscaling, 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, while optimizing costs. Events like OOM or throttling are constantly monitored, and safety mechanisms such as rollback protection ensure workloads remain stable during optimization changes.
Is there a complex setup or onboarding process?
How long does it take to see savings after implementation?
Recommendations are available about 24 hours after connecting a cluster to Zesty platform. Once a recommendation is activated, multi-dimensional autoscaling is fully automated. Users start seeing measurable savings under one hour after activation.
Optimize at every layer
Compute cost visibility
Analyze workload costs, identify waste, and automate with clear data-driven insights.
Azure Commitment Optimization
Continuously align commitments with usage to maximize coverage and minimize lock-in risk.
PV autoscaling
Automatically rightsize persistent volumes based on usage to reduce costs and maintain availability.
FastScaler
Reduce app startup time by up to X5 to absorb spikes without throttling or OOMs.
Adaptive pod placement
Reposition unevictable pods to reduce fragmentation and enable node consolidation.
AWS Commitment Optimization
Continuously align commitments with usage to maximize coverage and minimize lock-in risk.
