Right-Size Every GKE Workload, Autonomously
GKE removes the operational burden of running Kubernetes, but resource decisions like pod requests, node packing, and GPU allocation still drive your bill. Sedai continuously analyzes actual usage and safely adjusts resources in both Standard and Autopilot clusters, so you stop paying for capacity you don't use.
GKE Makes Kubernetes Easier to Run. Not Easier to Right-Size.
Google manages the control plane, but the resource decisions that drive cost are still yours to get right, and most teams don't have the bandwidth to do it continuously.
Workloads are over-provisioned by default.
CPU and memory requests get set from worst-case estimates and rarely revisited, wasting resources regardless of GKE mode.
That waste hits your bill differently depending on mode.
Standard leaves you paying for underutilized nodes; Autopilot bills you for requested resources whether you use them or not.
Fixing it manually doesn't scale, and specialized resources make it worse.
GPUs and high-performance disks are frequently over-provisioned for peak loads that rarely occur.
How We Help
Autonomous Workload Rightsizing
Sedai analyzes historical CPU and memory usage for every workload and continuously adjusts resource requests to match real demand, in both Standard and Autopilot clusters.
Smarter Node Infrastructure (Standard)
Sedai consolidates pods onto fewer, better-utilized nodes, helps the cluster autoscaler terminate idle capacity faster, and recommends more cost-effective instance types for your node pools.
GPU & Persistent Disk Optimization
Sedai identifies idle GPU capacity, right-sizes workloads to the most cost-effective GPU types, and flags overprovisioned or unattached Persistent Disks based on real I/O patterns.