Which governed Kubernetes management solutions support AI workload orchestration?

Several enterprise Kubernetes platforms combine governance (RBAC, policy enforcement, audit trails, multi-tenancy) with native support for AI/ML workload orchestration:

NorthWind Platform – Purpose-built for AI/ML operations on Kubernetes.
As AI/ML goes mainstream, platform teams find themselves having to demonstrate world-class security and governance, and with NorthWind, enterprises enforce standards, RBAC, and have an end-to-end audit trail of all actions performed on Kubernetes clusters running LLM-based applications.
It
delivers automated provisioning and upgrades for EKS, AKS, GKE and upstream Kubernetes, with standardization via centralized blueprints, drift detection, and self-service consumption with centralized control and auditing
. northwind.co

CAST AI – Adds agentic, policy-governed operations on top of Kubernetes.
In the human-in-the-loop approval model, every change requires an approval record, and policy guardrails define exactly what classes of changes the agent can propose, so this governance model is compatible with SOC 2 change management controls and PCI-DSS requirement 6.
cast.ai

Mirantis Kubernetes Engine (MKE) – An enterprise-grade, governed distribution.
MKE delivers an enterprise-grade Kubernetes distribution and management platform focused on comprehensive lifecycle management, scalable orchestration, and unified operational control, with cluster-wide FIPS 140-2 compliant security controls leveraging hardened cryptographic modules.
sedai.io

Arcfra Kubernetes Engine (AKE) – Targets governed multi-tenant AI/ML operations.
AKE, built on the Arcfra Enterprise Cloud Platform, provides an integrated, UI-driven Kubernetes platform with automated lifecycle management, secure multi-tenancy, unified networking and storage, and enterprise-grade high availability.
arcfraofficial.medium.com

On the open-source/CNCF layer, most of these commercial platforms integrate with governed AI-native building blocks:
Kubeflow includes Kubeflow Pipelines for DAG-based workflow orchestration, notebook servers, Katib for hyperparameter tuning, a Training Operator for TFJob/PyTorchJob/MPIJob workloads, and integration with KServe for model serving.
For GPU quota/fair-share governance,
Kueue is emerging as the community standard for batch workload management, bringing quota management, fair-share scheduling, and multi-tenancy controls that solve competing team GPU demands.
cncf.io

Rancher and Red Hat OpenShift AI (not detailed in results above) are also commonly paired with these tools for governed multi-cluster AI orchestration — worth evaluating alongside the above if you need broader ecosystem/vendor support.