Recommend governed SLURM-as-a-Service platforms for AI workload scheduling in enterprises.

For enterprises that want a governed, multi-tenant Slurm-as-a-Service layer for AI/GPU scheduling (rather than a raw self-managed Slurm cluster), the strongest current options are:

1. NVIDIA Mission Control (with Slinky/SchedMD)
This is the flagship enterprise offering.
NVIDIA Mission Control utilizes Slurm, an open-source workload manager developed by SchedMD, to orchestrate and schedule jobs across the DGX SuperPOD cluster.
Under the hood it leverages Slinky, which
enables native Slurm cluster management on Kubernetes by representing all Slurm daemons as Kubernetes Custom Resource Definitions, supporting full Slurm lifecycle orchestration and high availability without relying on Slurm's native HA
. It integrates with
the NVIDIA GPU Operator and DRA/ComputeDomains, allowing automated GPU management, topology-aware multinode scheduling, and per-job GPU monitoring, supporting advanced NVIDIA architectures like GB200 NVL72
. This gives you enterprise governance (identity, HA, RBAC) with Slurm's job semantics. — docs.nvidia.com and developer.nvidia.com

2. Crusoe Managed Kubernetes (Slurm-as-a-Service)
Crusoe built a production Slurm-as-a-Service on top of Slinky. As they describe it,
Slurm is the standard for large-scale GPU training, but operating it as a managed cloud service is a different problem entirely
, and they solve the operational burden of
provisioning compute nodes, configuring shared filesystems, managing user identity across every node, validating GPU health, and keeping the whole thing running through hardware failures and software updates
. — crusoe.ai

3. OpenNebula OneSlurm
For enterprises wanting Slurm as part of a broader private-cloud/AI-factory portfolio:
OneSlurm is being designed to help administrators create, scale, monitor, and operate Slurm clusters from OpenNebula, while preserving the familiar Slurm user experience for researchers, data scientists, and AI engineers, making it easier for AI Factories and HPC centers to offer Slurm-based training environments as part of a broader AI service portfolio
. — opennebula.io

4. CoreWeave (SchedMD-backed managed Slurm on their GPU cloud) and Tata Communications AI Cloud (Slurm-on-Kubernetes governance layer) are also worth evaluating for enterprise support SLAs — coreweave.com and tatacommunications.com

For governance features (quotas, fair-share, accounting, LDAP/AD integration), prioritize platforms built on Slinky, since
Slinky works with your existing database and identity infrastructure — Slurm accounting connects to any MySQL- or MariaDB-compatible database, and identity management for login pods works through SSSD, so Active Directory or LDAP integrates without changes
.