Kubernetes Optimization Engine (KOE)

Autonomous Kuberenetes optimization.

Reyki AI - Kubernetes Optimization Engine (KOE)

Reyki AI continuously monitors, analyzes, and optimizes your Kubernetes implementation.

Reyki AI streamlines your Kubernetes implementation using these core techniques: workload rightsizing, demand-based scaling, automatic bin packing, auto discount coverage, resource time-to-live policies, and 24/7 real-time utilization monitoring. When harmonized effectively, these methods not only significantly curtail wasteful spending and resource idling but also substantially bolster your application's resilience and stability.

  • Automatically align resource sizes to match actual utilization, requests, and limits to enhance application stability and minimize resource waste.

  • Automatic horizontal scaling adjusts the number of pods according to resource usage.

  • Automatic vertical scaling fine-tunes per-pod sizes based on resource demands.

  • Automatically tune cluster size and resources in response to fluctuating workload schedules; seamlessly ramp up during high-demand periods and scale down during off-peak periods.

  • Automatically compact pods into fewer nodes via optimized resource allocation, thereby freeing up empty nodes for automatic termination and drive significant costs.

  • Automatically calculate and apply unit price discounts based on cloud service provider's spot rates or usage-based discounts to further maximize savings.

  • Automatically set time-to-live policies for detected temporary Kubernetes resources, environments, and deployments to reduce unnecessary zombie costs.

  • Automatic resource utilization monitoring and tuning minimizes costs without customer effort.

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