Backend.AI
6.3#4 in gpu cluster management softwareFree planFree planWebAPILinuxSelf-hosted
VisitBackend.AI vs dstack
What both publish
| Fact | Backend.AI | dstack |
|---|---|---|
| Accelerators | The platform says its hardware abstraction layer supports more than 12 accelerator types, including NVIDIA, AMD, Intel, Rebellions, and FuriosaAI products. | dstack supports NVIDIA, AMD, TPU, and Tenstorrent accelerators out of the box. |
| Deployment | Backend.AI can be deployed on-premises, in public cloud environments, or in a hybrid setup. | The server can run on a laptop or another environment with access to the cloud and on-prem clusters being used. |
| Purpose | Backend.AI is an AI infrastructure platform for building, training, and serving models and running parallel computing jobs. | dstack is an open-source orchestration layer for AI workloads on heterogeneous accelerators, including GPU clouds, Kubernetes, VMs, and bare-metal clusters. |
| Support | Lablup invites organizations to contact its team for help planning AI clusters and scaling production workloads. | The documentation directs users to report issues on GitHub and ask questions in the dstack Discord server. |
Backend.AI plans
- Open-sourceFree
- Try-outFree
- Enterprise (cloud)Priced on request
- Enterprise (on-premises)Priced on request
dstack plans
- dstack OSSFree
- dstack Sky GPU MarketplacePriced 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 dstack if…
dstack suits teams orchestrating AI workloads across different accelerator and infrastructure environments. It is also suited to users who want to configure workloads with YAML and manage resources through a CLI or HTTP API.