Backend.AI
6.3#4 in gpu cluster management softwareFree planFree planWebAPILinuxSelf-hosted
VisitBackend.AI vs HAMi
What both publish
| Fact | Backend.AI | HAMi |
|---|---|---|
| Accelerators | The platform says its hardware abstraction layer supports more than 12 accelerator types, including NVIDIA, AMD, Intel, Rebellions, and FuriosaAI products. | The project lists support for NVIDIA, AWS Neuron, Huawei Ascend, Cambricon, Enflame, Hygon, Iluvatar, Kunlunxin, MetaX, Moore Threads, Vastai, AMD and Biren accelerators. |
| Deployment | Backend.AI can be deployed on-premises, in public cloud environments, or in a hybrid setup. | The quick start installs HAMi with Helm and requires Kubernetes, Helm, kubectl, and installation permissions. |
| Purpose | Backend.AI is an AI infrastructure platform for building, training, and serving models and running parallel computing jobs. | HAMi is open-source, cloud-native GPU virtualization middleware for sharing, isolating, and scheduling heterogeneous accelerators on Kubernetes. |
| Support | Lablup invites organizations to contact its team for help planning AI clusters and scaling production workloads. | The site links to documentation, tutorials, Discord and the `#hami-dev` Slack channel for community resources. |
Backend.AI plans
- Open-sourceFree
- Try-outFree
- Enterprise (cloud)Priced on request
- Enterprise (on-premises)Priced on request
HAMi plans
- HAMiFree
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 HAMi if…
HAMi suits teams running AI, machine-learning or HPC workloads on Kubernetes that need to share accelerators and manage GPU quotas. It is also relevant to operators who want device utilization metrics and scheduling integrations.