How to Move a Databricks or SageMaker Training Pipeline to Kubernetes (Without Losing a Month)

Moving Databricks or SageMaker training pipelines to Kubernetes can reduce sustained GPU-training costs and increase control over CUDA versions, drivers, GPU partitioning, spot capacity, autoscaling, and multi-node networking, but it transfers operational responsibility to the organization. The proposed migration replaces managed platform layers for orchestration, distributed training, GPU runtime, storage, experiment tracking, and identity with tools such as Argo, Kubeflow, Flyte, NVIDIA GPU Operator, object storage, MLflow, and cloud workload identity. It recommends a staged seven-step approach: containerize code, externalize data paths, create GPU node pools, validate one GPU job, port orchestration and tracking, then parallel-run a full retraining cycle before cutover. The article argues that Kubernetes avoids SageMaker’s 15–40% instance premium and Databricks DBU charges while enabling spot instances, MIG GPU sharing, scale-to-zero infrastructure, and account-level cloud discounts, though idle GPUs, platform engineering effort, and cluster maintenance can eliminate these savings. It advises remaining on managed services for low utilization, limited operational expertise, or deep integration with Unity Catalog or SageMaker Feature Store, and presents hybrid deployments that retain managed data governance while moving GPU training to Kubernetes. Successful cutover should verify model parity, training throughput, completed-run cost, GPU utilization, operational ownership, checkpointing, and rollback readiness, while Qovery is presented as an option for operating Kubernetes infrastructure in a customer-owned cloud account.

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