Open-source MLOps toolkit for running end-to-end ML workflows on Kubernetes. By 2026, Kubeflow Pipelines and KServe remain a popular self-hosted choice for orga
Kubeflow is the open-source MLOps platform on Kubernetes: pipelines, notebooks, distributed training operators, and KServe model serving, the self-hosted, portable answer to managed ML platforms for organizations that own their infrastructure.
Pricing: Open-source free; cost is the engineering to run it (or vendor distributions with support).
Kubeflow for portability, sovereignty, and unit cost at scale: paid for in platform engineering; SageMaker for managed velocity inside AWS. Regulated and multi-cloud orgs skew Kubeflow.
Yes for training/serving infrastructure; teams typically pair it with LLM-specific layers (vLLM serving, MLflow tracking, eval tooling) rather than expecting one platform to cover all.