Backend.AI
6.3#4 in gpu cluster management softwareFree planFree planWebAPILinuxSelf-hosted
VisitBackend.AI vs NVIDIA Mission Control
NVIDIA Mission Control
5.4#16 in gpu cluster management softwareWebLinuxSelf-hosted
VisitWhat both publish
| Fact | Backend.AI | NVIDIA Mission Control |
|---|---|---|
| Deployment | Backend.AI can be deployed on-premises, in public cloud environments, or in a hybrid setup. | The 2.3 release adds an option for a virtualized control plane and deployment in air-gapped environments. |
| Founded | 2015 | 1993 |
| Headquarters | Seoul, South Korea | Santa Clara, California, USA |
| Intended users | Lablup describes its audience as ranging from small teams with a single GPU to institutions running clusters of thousands. | NVIDIA describes Mission Control for enterprise AI infrastructure, AI architects and HPC operators managing AI factories. |
| Purpose | Backend.AI is an AI infrastructure platform for building, training, and serving models and running parallel computing jobs. | NVIDIA Mission Control is an integrated AI factory management platform for enterprise AI infrastructure that combines cluster automation and operational best practices in one control plane. |
| Support | Lablup invites organizations to contact its team for help planning AI clusters and scaling production workloads. | NVIDIA directs Mission Control customers to NVIDIA Enterprise Support Services for expert support and guidance. |
Backend.AI plans
- Open-sourceFree
- Try-outFree
- Enterprise (cloud)Priced on request
- Enterprise (on-premises)Priced on request
NVIDIA Mission Control plans
- NVIDIA Mission ControlPriced on request
Pick Backend.AI if…
Backend.AI suits teams or institutions running GPU workloads, from single-GPU home use to larger clusters. It also fits organizations seeking on-premises, cloud, hybrid, or air-gapped deployment options.
Pick NVIDIA Mission Control if…
NVIDIA Mission Control is intended for enterprise AI infrastructure teams, AI architects, and HPC operators managing AI factories. It is relevant to organizations using Slurm or Kubernetes for multi-node scheduling.