Full Control. Now With Autonomous Optimization.
Self-managed Kubernetes gives you full control of the stack — and full responsibility for optimizing it. Sedai adds autonomous rightsizing without asking you to give up that control.
On-Prem Kubernetes Wasn't Built to Tune Itself
Rightsizing self-managed clusters still relies on static estimates and manual effort — with no cloud elasticity to absorb the guesswork.
Static Sizing Wastes Capacity
Workloads run on worst-case CPU and memory requests that are set once and rarely revisited.
Manual Tuning Doesn't Scale
Rightsizing by hand is slow, guesswork-driven, and risky at the pace modern clusters change.
Toolchains Don't Optimize Themselves
HPA and GitOps controllers execute configs but can't tune themselves for cost or performance.
How We Help
Autonomous Workload Optimization
Sedai continuously analyzes utilization and performance to safely adjust pod and container resource allocations — using reinforcement learning to move workloads toward their optimal state.
GPU Optimization
For AI, ML, and HPC workloads, Sedai identifies idle GPU capacity and right-sizes allocations to match real demand, cutting a major cost driver.
Works With Your Existing Toolchain
Sedai doesn't replace HPA or your GitOps controller — it refines their configurations continuously, connecting via a lightweight agent or GitOps integration to fit your security needs.