Backend.AI vs OpenPBS
Backend.AI
backend.aiOpenPBS
openpbs.orgSide by side
| Figure | Backend.AI | OpenPBS |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | — |
| macOS | — | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 4 | 1 |
| Spec score · GPU Cluster Management Software | 8.3 (#3) | 7.6 (#9) |
| Purpose | Backend.AI is an AI infrastructure platform for building, training, and serving models and running parallel computing jobs. | OpenPBS optimizes job scheduling and workload management in high-performance computing environments. |
Plans
Backend.AI
- Open-sourceFree
- Try-outFree
- Enterprise (cloud)Priced on request
- Enterprise (on-premises)Priced on request
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.
OpenPBS
OpenPBS schedules jobs and manages workloads in high-performance computing environments, including clusters, clouds, and supercomputers. Its policy-driven scheduling balances job turnaround time and system utilization while selecting job placements. The system is designed for millions of cores and has been tested beyond 50,000 nodes. A plugin framework provides administration visibility and supports customized extensions. Health checks monitor faults and can mitigate them automatically; the fail-over architecture is designed to avoid a single point of failure and keep jobs running through failures. OpenPBS is self-hosted and free under the AGPL 3.0 open-source license, while PBS Pro is also dual licensed commercially. Public-sector organizations such as academic and research facilities generally choose the open-source license. Community help comes through a volunteer forum, without a guarantee of answers or bug fixes. OpenPBS is integrated into the OpenHPC software stack. The installation guide states that SELinux is not supported, and critical and high security fixes are delivered in the next major release.