Backend.AI
Backend.AI: A broad GPU cluster platform for teams managing shared, multi-tenant AI infrastructure. Ranked #1 of 21 in GPU Cluster Management Software by our editors (9.4/10); pricing: Free plan; best for teams running shared AI GPU infrastructure.
At a glance
- Editor score9.4 / 10
- PricingFree plan
- Best forTeams running shared AI GPU infrastructure
- Free planYes
- Deployment modelHybrid
- Founded2015 · Seoul, South Korea
- Facts checked22 Sep 2026
Where it wins
- Schedules interactive, batch, and inference workloads across GPU clusters
- Supports fractional GPU virtualization, quotas, monitoring, and idle-session reclamation
- Offers open-source, self-hosted, on-premises, cloud, and hybrid deployment options
Where it doesn't
- Enterprise and Cloud Enterprise pricing is quote-based
- Self-hosted deployment requires the organization’s own hardware
- The Try-out plan uses Lablup resources rather than your own infrastructure
Our verdict on Backend.AI
Backend.AI is an AI infrastructure platform from Lablup Inc. for research teams, universities, enterprises, and infrastructure operators managing shared GPU and heterogeneous accelerator clusters. It combines containerized compute sessions with cluster-level scheduling through the Sokovan orchestrator, fractional GPU virtualization, quotas, monitoring, and model-serving endpoints. Teams can manage interactive, batch, training, inference, and multi-tenant workloads through a web interface and APIs, with deployment options spanning self-hosted, on-premises, cloud, and hybrid installations.
Its main strength is breadth across the operational lifecycle. Administrators can set per-team GPU quotas and session time policies, monitor GPU utilization, node status, and sessions, and use automatic resource allocation with idle-session reclamation. Backend.AI also supports model deployment as scalable API endpoints. Platform fit extends through Kubernetes, Docker, Harbor, SSO integrations, and SDKs for Python, JavaScript, Java, and PHP. That combination suits organizations that need one control layer for varied workloads and user groups rather than a tool focused only on scheduling or model serving.
The published plans cover several operating models. The Open-source plan supports installation on your own hardware and includes an open-source version for home users. Enterprise adds on-premises deployment, enterprise-specific features, and GPU cluster allocation across organizations and users. Try-out provides a cloud service trial using Lablup resources, while Cloud Enterprise adds cloud deployment, pre-scheduled resources, user and group management, and resource allocation. Backend.AI is a strong fit for teams coordinating shared AI infrastructure across users and workload types. Organizations seeking a narrowly focused scheduler, or teams that prefer a fully managed service without infrastructure responsibilities, should compare those needs against its deployment choices.
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Backend.AI pricing
All 4 Backend.AI plans and prices →
Backend.AI fact sheet
| Free plan | Yes |
|---|---|
| Paid from | Not verified |
| Deployment model | Hybrid |
| Workload scheduling | Both |
| Kubernetes support | Yes |
| Quota controls | Yes |
| GPU utilization metrics | Yes |
| Cloud GPU support | Yes |
| Deployment | Cloud, Self-hosted |
| Platforms | Web |
| Support | Docs |
| Built for | Small business, Mid-market, Enterprise (editorial estimate) |
| Integrations | 8 integrations: Kubernetes, Docker, Harbor, SSO integrations, Python SDK, JavaScript SDK … |
| Pricing | Free plan (source) |
| Website | backend.ai |
| Facts checked | 22 Sep 2026 |
Backend.AI integrations
Backend.AI lists 8 integrations on its own site.
- Kubernetes
- Docker
- Harbor
- SSO integrations
- Python SDK
- JavaScript SDK
- Java SDK
- PHP SDK
Alternatives to Backend.AI
- NVIDIA Mission ControlEnterprise GPU cluster management with orchestration, observability, and automated recovery.9.2
- NVIDIA Run:aiA broad Kubernetes-native GPU control plane for training, inference, and shared infrastructure.9.0
- dstackBroad GPU orchestration for teams spanning cloud, Kubernetes, Slurm, and on-premises systems.8.8
See all Backend.AI alternatives →
Backend.AI vs the competition
- Backend.AI vs NVIDIA Mission Control
- Backend.AI vs NVIDIA Run:ai
- Backend.AI vs dstack
- Backend.AI vs Volcano
- Backend.AI vs Apache YuniKorn
- Backend.AI vs KAI Scheduler
Compare Backend.AI with any tool side by side →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
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