Learn how Sedai autonomously optimizes Kubernetes (K8s) clusters and workloads to achieve your performance, cost, and availability goals.

Sedai uses the following features to intelligently optimize your K8s clusters (self-managed or hosted) at both workload and container levels. Sedai supports EKS, ECS (EC2-based or Fargate), GKE (Standard or Autopilot) and AKS clusters.

Cost Optimization and Resource Efficiency

Sedai autonomously optimizes K8s clusters and workloads to improve both cost and resource efficiency. This is achieved by:

  1. Rightsizing deployments: Sedai determines the optimal CPU and memory for a particular workload and then allocates these resources accordingly to prevent excessive costs.

  2. Optimizing cluster configurations and node groups: Sedai optimizes cluster CPU & memory configurations and determines the optimal number of node groups for peak efficiency.

When generating optimization opportunities for Kubernetes clusters, Sedai will take into consideration any predefined parameters for node groups, which can be set from Cluster Constraints in settings.

Performance Optimization

In addition to optimizing cost, Sedai autonomously improves application performance. Sedai’s intelligent and continuous horizontal and vertical scaling increases performance efficiency, allows for greater scalability, and ultimately improves overall reliability.

To view optimization opportunities for Kubernetes, go to Optimization > Opportunities and select the Containers tab.

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