NVIDIA Run:ai vs Mission Control
Run:ai schedules AI workloads and shares GPUs across teams on Kubernetes. Mission Control runs a whole NVIDIA AI factory, from cluster deployment and health checks to recovery and power, and includes Run:ai technology for workload orchestration. They complement each other rather than compete.1
How they relate
Complementary. They do different jobs and are often used together.
Run:ai answers the question of who gets which GPUs and when. It is NVIDIA's platform for AI workload and GPU orchestration: a control plane, offered as a managed SaaS service or self-hosted, connects to a Run:ai cluster component that runs as a Kubernetes application. NVIDIA describes pooling GPU resources, policy-driven governance across teams and fractional GPU allocation for inference.
Mission Control answers a wider question: how to keep an AI factory running. NVIDIA describes it as software that covers developer workload scheduling and orchestration, continuous health checks, an autonomous recovery engine, power policies and building management integration for Blackwell and Rubin data centers. Version 2.3 supports GB200 NVL72 and GB300 NVL72 systems.
The overlap is workload orchestration, and NVIDIA resolves it directly: Mission Control includes Run:ai technology and also lets customers integrate Slurm or bring their own Kubernetes. NVIDIA AI Enterprise includes Run:ai as well. The choice depends on scope: GPU sharing on clusters you already operate, or full operations software for NVIDIA rack-scale systems. There is no universal winner.234
The technologies
Platform
NVIDIA Run:ai
NVIDIA Run:ai is a Kubernetes-based platform that pools GPUs and schedules AI workloads across teams using quotas, priorities and fair sharing, so a shared cluster can serve notebooks, training and inference without each team owning fixed hardware.
Platform
NVIDIA Mission Control
NVIDIA Mission Control is operations software for AI factories built on DGX and GB200/GB300 NVL72 systems. It brings cluster provisioning, Slurm and Kubernetes scheduling, health checks, automated recovery, power policies and building management integration into one supported control plane.
Which fits which goal
| If your goal is | What fits | Why |
|---|---|---|
| Share a Kubernetes GPU cluster fairly across several teams and projects | Run:ai | Run:ai pools GPU resources and applies policy-driven governance for prioritized access across departments, projects and teams, and its cluster component installs on Kubernetes.1 |
| Operate GB200 or GB300 NVL72 racks with health checks and automated recovery | Mission Control, which includes Run:ai technology | Mission Control 2.3 supports GB200 NVL72 and GB300 NVL72 and adds continuous health checks with automated actions and an autonomous recovery engine.4 |
| Keep Slurm for training while adding cluster operations tooling | Mission Control, or your existing Slurm setup if operations tooling is not the gap | NVIDIA says the Mission Control software stack supports multi-node scheduling with both Slurm and Kubernetes.4 |
| Already licensed for NVIDIA AI Enterprise and want GPU orchestration | Run:ai | NVIDIA states that AI Enterprise now includes Run:ai.1 |
| A small team wants Kubernetes GPU scheduling without a commercial platform | None of these: the open source KAI Scheduler, which is based on Run:ai | NVIDIA describes KAI Scheduler as open source, based on Run:ai and suited to developers and small teams.1 |
No technology here is ranked. Pricing and performance are left out on purpose: they depend on your models, hardware and contract, so measure them in a pilot.
Sources
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