From Pods to Node Pools, Optimized Together
AKS workloads are provisioned once against worst-case estimates, and the node pools underneath inherit that waste — running oversized, mismatched, or underutilized. Sedai continuously rightsizes pods and containers, then optimizes node type, count, and segregation to match, while attributing cost back to the teams responsible.
Rightsizing Pods Doesn't Fix a Cluster on Its Own
Kubernetes workloads and the infrastructure underneath them are two separate optimization problems, and most teams only have the bandwidth to guess at one — usually with static, worst-case requests that ripple all the way down to the node pool.
Static resource requests set the ceiling for everything above and below them.
Teams size pods for worst-case load and rarely revisit it, and manually rightsizing dozens of clusters on an ongoing basis is guesswork that carries risk of causing performance issues or outages if done too aggressively.
Node pools inherit whatever inefficiency the workloads create — and add their own.
VM instance types that don't match the workload profile, underutilized pools that never get consolidated, and mixed workloads sharing infrastructure they shouldn't all compound the waste from oversized pods.
Shared clusters obscure who's actually spending what.
Without workload-level attribution, the costs of compute, storage, GPU, and network usage in a shared cluster are difficult to trace back to the team or business unit responsible for them.
How We Help
Autonomous Workload Optimization
Sedai analyzes real-time CPU and memory data and continuously adjusts pod scaling through small, validated changes, moving workloads to their optimal state without disruption.
Node & Cluster Optimization
After rightsizing workloads, Sedai recommends better-matched instance types, resizes or consolidates node pools, and suggests splitting mixed workloads onto more specialized infrastructure.
Cost Attribution & Application-Aware Tuning
Get workload-level cost attribution across compute, storage, and network, with trade-offs tuned to whether a workload is latency-sensitive or cost-sensitive.