Optimize Live

Autonomous Rightsizing for Kubernetes Workloads

Horizontal Autoscaling PERFORMANCE. Vertical Autoscaling EFFICIENCY.

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Free trial includes full version on 1 cluster for 30 days

Maximize efficiency and ensure performance with bi-directional autoscaling

Unlike basic vertical autoscaling approaches (such as VPA) that conflict with Kubernetes’ built-in Horizontal Pod Autoscaler (HPA), Optimize Live uses machine learning to continuously harmonize requests and limits with standard HPA utilization targets, giving you the best of both worlds:

HORIZONTAL pod autoscaling replicates pods in seconds, ensuring performance when faced with a sharp spike in demand.

VERTICAL pod autoscaling continuously aligns resources to application demand as utilization ebbs and flows over hours, days and weeks.

Learn more in the Blog

Rightsize Accurately

Machine learning analyzes resource utilization every 15 seconds, building a fine-grained understanding of each new workload’s demand patterns in just 1-2 weeks for efficient sizing that maintains or improves application performance, availability and resilience.

Preview Impact to Gain Trust

Seeing is believing. Compare how Optimize Live’s recommended settings would have performed against your current settings, including resource consumption, utilization and simplified cost savings.

Simplify at Scale

Intelligent enough to plug-and-play

Begin optimizing clusters in minutes with automatic discovery, machine learning analysis, and recommendations dependably accurate enough to approve and apply after just 1-2 weeks.

No human configuration or tuning required.

Autonomous at Planet Scale

Put estate-wide rightsizing on autopilot with hands-free lifecycle automation. Optimize Live automatically discovers each new pod, captures its metrics, learns its patterns, calculates optimum settings, and applies them.

After investing 2 minutes in onboarding a cluster, no further manual steps are required.


Tuning optional

You may opt to tailor optimization for certain high-profile or outlier workloads: select an optimization goal for extra performance or extra efficiency, specify resizing frequency, set min/max requests, add include/exclude resource settings such as limits.

Apply your way

Apply recommended settings directly to pods through an agent in each cluster or export them to HELM or raw YAML and integrate with your CI/CD tool of choice.

Unify visibility and control

See all your pods in one place. Select automations, track resource savings, and opportunities across clusters, namespaces, nodes, and object types.

Seeing is Believing

Start getting resizing recommendations minutes from now

Start a TrialEnter SandboxWatch an Install (2 minutes)

Free trial includes full version on 1 cluster for 30 days