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.
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 continuously tunes pod CPU and memory requests based on actual consumption, eliminating static worst-case allocations and the waste that comes with them.
Node & Cluster Optimization
Sedai selects optimal instance types, redistributes workloads, and applies Cluster Compaction to maximize node utilization and reduce compute spend.
Cost Visibility & Purchasing
Get workload-level cost attribution across compute, GPU, storage, and network, plus purchasing recommendations across on-demand, savings plans, and reserved terms.