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KAI Scheduler vs Voda Scheduler

Both stand on gpu cluster management software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.

KAI Scheduler

Free plan Self-hosted

Choose KAI Scheduler if you need to allocate GPUs across Kubernetes AI workloads with queue policies, gang scheduling, and GPU sharing. Look elsewhere if you need a managed service or enforced runtime GPU memory isolation in the default NonMemoryEnforced mode.

Visit KAI Scheduler

Voda Scheduler

Paid APISelf-hosted

Voda Scheduler targets GPU-based distributed training, with runtime scaling, topology-aware scheduling, and several algorithms. Its experimental status and stated Kubernetes v1.20 testing are important considerations for deployment.

Visit Voda Scheduler

The facts

KAI SchedulerVoda Scheduler
Free planYesNot published
Open sourceNot publishedNot published
Runs onSelf-hostedAPI, Self-hosted
Lowest paid planNot publishedNot published
Plans publishedNoneNone
MakerNot publishedNot published
Websitekai-scheduler.devgithub.com
On gpu cluster management software#14 of 21#18 of 21

KAI Scheduler: what it does well

  • Open-source Apache-2.0 project with no listed price.
  • Supports training, inference, and data-processing workloads.
  • Hierarchical queues provide quotas, limits, priorities, and borrowing.
  • GPU sharing supports time slicing, MPS, and MIG.
  • Runs on cloud and on-premises infrastructure.

What to know first

  • Requires a running Kubernetes cluster and Helm CLI.
  • GPU-requesting workloads require NVIDIA GPU Operator.
  • Self-hosted deployment requires infrastructure management.
  • Default NonMemoryEnforced mode does not enforce runtime GPU memory isolation.

Voda Scheduler: what it does well

  • Scales distributed training jobs while running
  • Offers eight scheduling algorithms
  • Tracks node additions and removals
  • Works with an existing autoscaler
  • Apache-2.0 license

What to know first

  • Project is experimental and may change significantly
  • Tested with Kubernetes v1.20

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