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Polyaxon vs Run:ai

Compare Polyaxon and NVIDIA Run:ai across AI workload scope, GPU scheduling, orchestration, metadata, and Kubernetes platform ownership.

Compare capabilitiesDiscuss your workload

Which platform fits

Choose Polyaxon when

The team needs an end-to-end workload and metadata platform, not only a GPU resource control plane.

Choose Run:ai when

Maximizing and governing shared GPU capacity is the central platform problem.

Evaluate the private beta when

Polyaxon should own lifecycle workflows while KAI, Kueue, or Volcano governs selected scheduling behavior.

Capability comparison

This table describes product scope and operating responsibility. It is not a benchmark or a count of integrations.

Primary scope

Polyaxon

AI workload execution, orchestration, tracking, registries, and scheduling on Kubernetes.

Run:ai

AI workload orchestration and resource optimization centered on GPU clusters.

Workload model

Polyaxon

Jobs, services, sandboxes, distributed runs, DAGs, and matrix runs share one declarative model.

Run:ai

Native Workspaces, Training, and Inference workloads plus supported and externally submitted Kubernetes workloads.

GPU scheduling

Polyaxon

Resource requests, queues, priorities, concurrency, presets, approvals, and cluster routing govern admission.

Run:ai

Quotas, over-quota fairness, preemption, node pools, gang scheduling, topology awareness, and GPU fractions specialize resource placement.

Experiment tracking

Polyaxon

Tracks parameters, metrics, logs, artifacts, visualizations, run state, and lineage.

Run:ai

Official workload surfaces emphasize resource monitoring, utilization, status, and workload operations rather than a full experiment system of record.

Workflow orchestration

Polyaxon

DAGs, matrix strategies, schedules, hooks, retries, and lifecycle automation are built in.

Run:ai

Orchestrates supported workload types and their resource lifecycle; multi-step ML pipeline authoring is a separate concern.

Registry and lifecycle metadata

Polyaxon

Models, artifacts, components, prompts, and datasets link back to producing runs.

Run:ai

Model and experiment registries are not the focus of the reviewed workload and scheduler documentation.

Deployment model

Polyaxon

Open source, self-hosted enterprise, or managed control plane connected to Kubernetes clusters.

Run:ai

SaaS and self-hosted product documentation cover cluster management, workloads, and scheduling.

Best fit

Polyaxon

Teams standardizing the complete path from workload definition through execution evidence and registries.

Run:ai

Infrastructure teams optimizing scarce GPU capacity across departments, projects, and workload types.

Relevant product previews

These previews may affect the decision, but they are not included as generally available capabilities in the comparison above.

Private beta

KAI, Kueue, and Volcano integrations

Polyaxon is actively integrating with KAI Scheduler, Kueue, and Volcano. The work is in progress and being tested with selected customers; it is not generally available yet.

KAI SchedulerKueueVolcano

Preview scope and timelines may change.

Ask about private access

When each platform fits

Choose Polyaxon when

  • The evaluation includes pipelines, tracking, artifact lineage, registries, reproducibility, and workload policy.
  • Users need one consistent model for CPU, GPU, service, distributed, batch, and interactive workloads.
  • Open-source access and a portable Kubernetes control plane are central requirements.

Choose Run:ai when

  • GPU quotas, over-quota allocation, fairshare, preemption, and specialized placement are the decisive capabilities.
  • The platform manages large shared accelerator estates across organizational departments and projects.
  • Native workspace, training, and inference experiences should be coupled directly to the GPU scheduler.

Using Polyaxon with Run:ai

There are two different layered designs to evaluate. For an open scheduler boundary, Polyaxon is actively integrating with KAI Scheduler, Kueue, and Volcano; that work is in private beta with selected customers and is not generally available. A separate design can pair Polyaxon's lifecycle layer with the commercial NVIDIA Run:ai platform, but that boundary still requires explicit validation.

  • Assign one scheduler as the authoritative admission and preemption owner for each workload.
  • Verify that Polyaxon workload manifests map cleanly to the selected KAI, Kueue, Volcano, or NVIDIA Run:ai interface.
  • Keep experiment identifiers and GPU allocation telemetry linked without duplicating run state.

Evaluation plan

  • Profile the constrained workloads

    Measure queue time, requested and used GPU memory, distributed topology, preemption tolerance, and service latency needs.

  • Exercise resource contention

    Run interactive, training, and inference workloads from two teams against realistic quota and priority policy.

  • Inspect the lifecycle gaps

    Compare metadata, artifacts, registries, pipeline authoring, recovery, upgrade ownership, and cost alongside utilization.

Sources

Product capabilities change. Follow the linked documentation for current details.

Compare against your requirements

We can map your current scheduler, tracking stack, storage, GPU policy, and migration constraints before you commit to a platform change.

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