Backend.AI vs KAI Scheduler

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.

KAI Scheduler is a self-hosted, open-source Kubernetes scheduler for allocating GPUs to AI workloads such as data processing, training, and inference. It supports interactive notebooks through multi-node distributed training in one cluster and is designed for large GPU clusters and high-throughput workloads. Hierarchical queue trees provide quotas, limits, priorities, and borrowing between teams. Gang scheduling places distributed workloads together when resources are available, while elastic workloads change replica counts within configured minimum and maximum values. GPU sharing includes time slicing, MPS, and MIG. Other scheduling controls include topology-aware placement, time-based fairshare, and priority-based preemption. KAI documents integrations with Ray on Kubernetes and Grove. It runs on cloud or on-premises infrastructure, including Karpenter environments. Installation uses a production Helm chart, source builds, or ArgoCD, with a running Kubernetes cluster, Helm CLI, and NVIDIA GPU Operator required for GPU requests. The Apache-2.0 project is also a CNCF sandbox project.