Multi-dimensional autoscaling

Cut Kubernetes costs

with unified horizontal

and vertical rightsizing

Continuously rightsizes CPU, memory, and replica counts together, eliminating resource waste to maximize Kubernetes infrastructure cost savings without sacrificing application performance.

Capabilities

Production-ready

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.

Compare policies before applying:

In-place pod resize

Built-in safety mechanisms

Update pod resource allocations without restarts. Gradual rollouts and automatic recovery mechanisms preserve stability during scaling events.

Customer Story

Our customers say it best

“We get over 40% optimization in cluster size, without having to manage it ourselves.”

Miguel Fontanilla

Platform Engineering Lead at Sennder

Benefits

Automation built for always-optimized clusters

Reduce compute costs

Continuously rightsize CPU, memory, and replica counts based on real workload demand to eliminate resource waste and reduce Kubernetes compute costs without manual tuning.

Enhance app performance

Maintain application performance during changing workloads by preventing CPU throttling, OOM events, and unstable scaling decisions while continuously rightsizing resources.

Eliminate manual tuning

Replace manual resource tuning with continuous rightsizing automation that safely reduces cloud costs while protecting workload performance.

Frequently Asked Questions

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.

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?
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 Zesty platform. Once a recommendation is activated, multi-dimensional autoscaling is fully automated. Users start seeing measurable savings under one hour after activation.