Optimize Amazon EKS Without the Guesswork
Most EKS teams set resource requests once, based on worst-case estimates, and never revisit them, leaving clusters chronically overprovisioned and expensive to run. Sedai continuously right-sizes pods, nodes, and purchasing to eliminate waste while protecting availability.
Sedai is an autonomous cloud optimization platform that continuously right-sizes Amazon EKS pods, nodes, and purchasing based on real workload behavior — not static rules or manual estimates. It works alongside Karpenter or Cluster Autoscaler, improving the signal quality your autoscaler relies on while eliminating the overprovisioning that drives up costs and corrupts scaling decisions. Sedai writes every change back to your Terraform, Helm, or Kustomize configs as a PR, so optimizations persist across deployments instead of drifting back.
EKS Optimization Isn't a One-Time Task. It's a Compounding Problem.
EKS removes control plane management — but node sizing, pod resource requests, and scaling configuration still fall on your team. Most clusters get provisioned for worst-case estimates and never revisited, creating downstream consequences that compound over time.
Overprovisioned pods corrupt your autoscaler's signals.
When resource requests are too high, Cluster Autoscaler adds nodes based on phantom demand — so oversized workloads drive an oversized cluster, and the two problems reinforce each other.
Manual optimization at cluster scale isn't sustainable.
Rightsizing pods across dozens of services requires ongoing analysis of actual behavior, not a one-time pass. By the time you finish, the workloads you started with have already drifted.
Getting it wrong has direct reliability consequences.
Too-low requests risk CPU throttling and OOM kills. Too-tight limits cause latency spikes under burst traffic. There's no safe direction to guess.
How We Help
Workload Right-Sizing
Sedai autonomously tunes pod CPU and memory requests based on real consumption patterns — P95/P99, not worst-case estimates. Changes are applied during safe maintenance windows with rollback if error rates spike. No VPA required.
Node & Cluster Optimization
Selects optimal instance types, redistributes workloads across nodes, and applies cluster compaction to eliminate underutilized nodes. Works with both Cluster Autoscaler and Karpenter — improving their decisions by feeding accurate pod requests upstream.
Cost Visibility & Purchasing
Maps workload-level cost attribution across compute, GPU, storage, and network. Recommends the right mix of on-demand, Savings Plans, Reserved Instances, and Spot based on actual usage stability — not blanket commitments.
IaC-Native Changes
Every optimization is written back to Terraform, Helm, or Kustomize as a pull request. Changes persist across deployments. No config drift. No "optimization that disappears on the next rollout."
Sedai vs Karpenter/EKS Auto Mode
Karpenter/EKS Auto Mode
- Provisions nodes based on pod requests. If those requests are wrong (and they usually are), Karpenter scales the wrong infrastructure — efficiently. It doesn't analyze actual workload behavior, adjust pod resources, or optimize purchasing. Node-level provisioning without pod-level intelligence compounds overprovisioning.
- Continuously right-sizes pod CPU and memory based on real consumption, then feeds accurate requests to Karpenter so it provisions the right nodes. Adds cluster compaction, purchasing optimization (Spot, Savings Plans, Reserved), and writes every change back to IaC as a PR. Karpenter handles where pods run. Sedai handles what they actually need — and keeps it current.