3 Matching Annotations
  1. Nov 2025
    1. How we slashed our EKS costs by 43% with one simple scheduler tweak 🚀
      • AWS EKS costs can escalate due to massive, parallel workloads in life sciences/drug development (e.g., genomic sequencing, molecular modeling).
      • Default Kubernetes scheduler uses leastAllocated strategy, spreading pods across many nodes for fairness/high availability.
      • leastAllocated strategy causes many partially utilized nodes, preventing autoscalers from scaling down idle nodes, increasing costs.
      • mostAllocated scheduling strategy "packs" pods onto fewer nodes, maximizing utilization and enabling autoscalers like Karpenter to remove idle nodes.
      • Switching to mostAllocated can reduce runtime costs significantly (e.g., ~10% in UAT, 43% in PROD environments).
      • Custom scheduler deployment on AWS EKS requires creating a service account, ClusterRoleBindings, RoleBinding, a ConfigMap with the mostAllocated scoring strategy, and a deployment with a matching Kubernetes version container image.
      • Resource weights can prioritize packing of expensive resources (e.g., high weight on GPUs for ML workloads).
      • Testing in non-production environments is recommended before full rollout.
      • Implementing mostAllocated scheduling can dramatically optimize costs by enabling cluster autoscalers to shut down unused nodes.
  2. Nov 2024
    1. Optimizing Kubernetes Costs with Multi-Tenancy and Virtual Clusters

      The blog post by Cliff Malmborg from Loft Labs discusses optimizing Kubernetes costs using multi-tenancy and virtual clusters. With Kubernetes expenses rising rapidly at scale, traditional cost-saving methods like autoscaling, resource quotas, and monitoring tools help but are not enough for complex environments where underutilized clusters are common. Multi-tenancy enables resource sharing, reducing the number of clusters and, in turn, management and operational costs.

      A virtual cluster is a fully functional Kubernetes cluster running within a larger host cluster, providing better isolation and flexibility than namespaces. Unlike namespaces, each virtual cluster has its own Kubernetes control plane, so resources like statefulsets and webhooks are isolated within it, while only core resources (like pods and services) are shared with the host cluster. This setup addresses the "noisy neighbor" problem, where workloads in a shared environment interfere with each other due to resource contention.

      Virtual clusters offer the isolation benefits of individual physical clusters but are cheaper and easier to manage than deploying separate physical clusters for each tenant or application. They also support "sleep mode," automatically scaling down unused resources to save costs, and allow shared use of central tools (like ingress controllers) installed in the host cluster. By transitioning to virtual clusters, companies can balance security, isolation, and cost-effectiveness, reducing the need for multiple physical clusters and making Kubernetes infrastructure scalable for modern, resource-demanding applications.

  3. Aug 2024
    1. Slashing Data Transfer Costs in AWS by 99%

      The essence of cutting AWS data transfer costs by 99% is to use Amazon S3 as an intermediary for data transfers between EC2 instances in different Availability Zones (AZs). Instead of direct transfers, which incur significant costs, you upload the data to S3 (free upload), and then download it within the same region (free download). By keeping the data in S3 only temporarily, you minimize storage costs, drastically reducing overall transfer expenses.