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SPECIFICTION
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Backend.AI vs NVIDIA Mission Control

Backend.AI

backend.ai

NVIDIA Mission Control

nvidia.com

Side by side

FigureBackend.AINVIDIA Mission Control
Price fromFree—
Free planYes—
Free trialYes—
Open source——
WebYesYes
LinuxYesYes
APIYes—
Self-hostedYesYes
Plans published41
Spec score · GPU Cluster Management Software8.3 (#3)7.2 (#15)
DeploymentBackend.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.
Founded20151993
HeadquartersSeoul, South KoreaSanta Clara, California, USA
Intended usersLablup 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.
PurposeBackend.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.
SupportLablup 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

Backend.AI pricing →

NVIDIA Mission Control

  • NVIDIA Mission ControlPriced on request

NVIDIA Mission Control pricing →

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.

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NVIDIA Mission Control

NVIDIA Mission Control has no write-up on Specifiction yet; its figures are above.

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