Backend.AI vs NVIDIA Mission Control
Backend.AI
backend.aiNVIDIA Mission Control
nvidia.comSide by side
| Figure | Backend.AI | NVIDIA Mission Control |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 4 | 1 |
| Spec score · GPU Cluster Management Software | 8.3 (#3) | 7.2 (#15) |
| 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. |
Plans
Backend.AI
- Open-sourceFree
- Try-outFree
- Enterprise (cloud)Priced on request
- Enterprise (on-premises)Priced on request
In their own words
Backend.AI
Backend.AI is an AI infrastructure platform for building, training, and serving models, as well as running parallel computing jobs. Its container-level GPU virtualization divides physical GPU capacity into fractional allocations so multiple users can share a GPU. The Sokovan scheduler supports multi-node, multi-tenant workloads and policy-based resource management. The platform says its hardware abstraction layer supports more than 12 accelerator types, including products from NVIDIA, AMD, Intel, Rebellions, and FuriosaAI. It can be deployed on-premises, in public clouds, or in a hybrid setup; installation, model deployment, and updates can operate without internet connectivity in air-gapped networks. The browser-based WebUI provides cluster management, resource and session monitoring, and policy-based allocation. Listed cloud environments include AWS, GCP, Azure, and OCI. Storage integrations include VAST, WEKA, Pure Storage, and IBM Storage Scale, with NVIDIA GPUDirect Storage support. Open-source is free for home users installing on their own hardware. Enterprise plans require a quote. Production guidance says compute nodes should be network-isolated with restricted inbound access.
NVIDIA Mission Control
NVIDIA Mission Control has no write-up on Specifiction yet; its figures are above.