Horizontal Autoscaling PERFORMANCE with Vertical Autoscaling EFFICIENCY
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Free trial includes full version on 1 cluster for 30 days!
Unlike basic vertical autoscaling approaches (such as VPA) that conflict with Kubernetes’ built-in Horizontal Pod Autoscaler (HPA), Optimize Live uses a forecast-based machine learning algorithm to continuously harmonize requests and limits with standard HPA utilization targets, giving you the best of both worlds:
Applications perform reliably and cost effectively — even under fluctuating demand — with infrastructure that is optimally scaled using an advanced workload forecasting machine learning algorithm that analyzes seasonal trends.
This goes far beyond basic statistical approaches. A fine-grained understanding of each new workload’s demand patterns is built in just a few days with resource utilization analyzed every 15 seconds.
Seeing is believing. Compare how Optimize Live’s recommendations would perform against your current settings, including resource consumption, utilization, and estimated cost savings.
Begin optimizing clusters in minutes with automatic discovery, machine learning analysis, and reliable recommendations ready to deploy in a week.
No human configuration or tuning required.
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.
Tailor optimization for specific business-critical or outlier workloads:
Ensure recommendations are always enforced alongside Argo or Flux. No need to change your manifests or pipelines — unless you want to.
See all your workloads in one place. Track resource savings and opportunities across clusters, namespaces, and workloads.
Start getting rightsizing recommendations minutes from now.
Watch An Install
Free trial includes full version on 1 cluster for 30 days!
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