Skip to content
SPECIFICTION
Get listed

Backend.AI vs GPUStack

Backend.AI

backend.ai

GPUStack

gpustack.ai

Side by side

FigureBackend.AIGPUStack
Price fromFreeFree
Free planYesYes
Free trialYes—
Open source——
WebYesYes
Windows—Yes
macOS—Yes
LinuxYesYes
APIYesYes
Self-hostedYesYes
Plans published41
Spec score · GPU Cluster Management Software8.3 (#3)8.3 (#4)
DeploymentBackend.AI can be deployed on-premises, in public cloud environments, or in a hybrid setup.It supports on-premise, Kubernetes, and multi-cloud GPU deployments, and the maker states that air-gapped deployments are supported.
HeadquartersSeoul, South KoreaShenzhen, Guangdong, China
SupportLablup invites organizations to contact its team for help planning AI clusters and scaling production workloads.The contact page offers personalized demos and architecture, deployment, and scaling guidance, and says replies typically arrive within one business day.

Plans

Backend.AI

  • Open-sourceFree
  • Try-outFree
  • Enterprise (cloud)Priced on request
  • Enterprise (on-premises)Priced on request

Backend.AI pricing →

GPUStack

  • GPUStack Community EditionFree

GPUStack 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.

Visit backend.ai

GPUStack

GPUStack is an open-source GPU cluster manager for deploying, governing, and scaling AI models across on-premise, cloud, or hybrid infrastructure. It provides OpenAI-compatible and Anthropic-compatible inference endpoints, with support for engines such as vLLM, SGLang, llama.cpp, TensorRT-LLM, and MindIE. Users can browse models from Hugging Face and ModelScope or supply local files, with automated compatibility checks. GPUStack can provision GPU instances with lifecycle controls, partitioning, SSH key injection, Jupyter access, and persistent S3 or NFS storage. Deployment options include on-premise, Kubernetes, and multi-cloud, including air-gapped deployments. The maker lists support for accelerators from NVIDIA, AMD, Ascend, T-head, Hygon, MetaX, Moore Threads, Cambricon, and Iluvatar. Security controls include RBAC, multi-tenancy, SSO, scoped API keys, IP allowlisting, token quotas, and usage analytics. GPUStack Community Edition is free. Worker nodes support Linux only; Windows users are directed to consider WSL2, and macOS worker nodes are not supported.

Visit gpustack.ai