Many teams leave SageMaker to gain more control over their GPU costs and platform flexibility. However, migrating to Kubernetes requires a shift from managed services to automated MLOps pipelines and Terraform-managed infrastructure.
The Migration Path
1. Model Export and Containerization
Move from SageMaker-specific formats to standard container images. This is the foundation of a portable ML infrastructure.
2. GPU Scheduling and Node Pools
On Kubernetes, you'll need to manage your own GPU node pools. Use NVIDIA Device Plugins to expose GPU resources to your pods and Karpenter or Cluster Autoscaler to manage node lifecycle.
3. Rebuilding Observability
SageMaker's built-in monitoring must be replaced with a Prometheus and Grafana stack that tracks both system health and model quality.
Final Takeaway
Migrating from SageMaker to Kubernetes is an opportunity to build a more efficient and customizable ML platform. By standardizing your release path and automating your infrastructure, you can reduce costs while improving your operational speed.
Planning a migration from SageMaker to Kubernetes? We help teams design low-risk migration paths, right-size GPU node pools, and build production-grade MLOps platforms. Book a free infrastructure audit and we’ll review your migration strategy.
