Polyaxon vs ClearML
Compare Polyaxon and ClearML across experiment tracking, agent-based execution, queues, pipelines, registries, and Kubernetes control.
Which platform fits
Choose Polyaxon when
Kubernetes workload policy and lifecycle metadata should share one declarative platform.
Choose ClearML when
Python experiment capture and agent-driven remote execution fit the team's existing workflow.
Use both when
ClearML-instrumented code should keep logging while Polyaxon becomes the Kubernetes execution owner.
Capability comparison
This table describes product scope and operating responsibility. It is not a benchmark or a count of integrations.
Primary scope
Polyaxon
Kubernetes AI workloads, orchestration, scheduling, tracking, and lifecycle registries.
ClearML
Experiment management, datasets, models, agent-based automation, pipelines, and workload orchestration.
Compute execution
Polyaxon
The control plane submits jobs, services, sandboxes, and distributed workloads to connected Kubernetes clusters.
ClearML
ClearML Agents pull tasks from queues, reconstruct environments, and execute on machines, Docker, or Kubernetes.
Experiment tracking
Polyaxon
Runs connect parameters, metrics, logs, artifacts, visualizations, code context, state, and lineage.
ClearML
Tasks capture experiment configuration, code, environments, metrics, artifacts, and models for comparison and reproduction.
Workflow orchestration
Polyaxon
Declarative DAGs, matrix runs, schedules, hooks, retries, and reusable components.
ClearML
Python controllers or decorators define pipeline steps that run locally or remotely through agent queues.
Scheduling policy
Polyaxon
Queues, priorities, concurrency, approvals, presets, resource requests, and multi-cluster routing are first-class.
ClearML
Ordered queues and agents support strict-priority or round-robin polling, with Kubernetes pod settings configurable per queue.
Registry and lineage
Polyaxon
Models, artifacts, components, prompts, and datasets remain linked to runs and workflows.
ClearML
The model registry catalogs versions, provenance, lineage, comparisons, publishing, and downstream automation.
Execution contract
Polyaxon
Containerized Kubernetes specifications and platform presets define reproducible execution.
ClearML
The agent can reconstruct Python environments or execute Docker images from captured task definitions.
Best fit
Polyaxon
Platform teams standardizing governed AI workloads on Kubernetes.
ClearML
ML teams prioritizing low-friction Python capture and agent-driven execution across mixed compute.
When each platform fits
Choose Polyaxon when
- Kubernetes resources, queues, approvals, distributed runtimes, services, and cluster routing drive the platform choice.
- Users need one declarative workload contract rather than environment reconstruction as the primary execution path.
- The organization wants execution policy, run state, artifacts, registries, and lineage under one operational owner.
Choose ClearML when
- Adding tracking to existing Python code with minimal workflow change is the fastest adoption path.
- Agents and queues should dispatch work across existing machines, Docker hosts, cloud autoscalers, or Kubernetes.
- Teams already depend on ClearML Tasks, datasets, pipelines, model registry, or environment reconstruction.
Using Polyaxon with ClearML
ClearML-instrumented code can run in a Polyaxon-scheduled container and log to a reachable ClearML Server. In that boundary, Polyaxon owns Kubernetes submission, resources, queues, retries, and workload state; ClearML owns its task record, metrics, artifacts, and model views. Avoid also enqueuing that same task for ClearML Agent execution.
- Choose Polyaxon or ClearML Agent as the execution owner for each task.
- Store the Polyaxon run identifier in ClearML task metadata and keep artifact locations unambiguous.
- Verify environment capture, cancellation, retry, offline logging, and failure propagation with a real workload.
Evaluation plan
Compare adoption paths
Instrument the same training script and record the code, environment, secrets, artifacts, and metadata each platform requires.
Exercise remote execution
Run on the intended Kubernetes queue or agent pool with a GPU request, dependency cache, retry, and cancellation.
Review operator ownership
Score cluster policy, environment reproducibility, pipeline control, lineage, upgrades, observability, and support.
Sources
Product capabilities change. Follow the linked documentation for current details.
Polyaxon overview
Workload, tracking, scheduling, pipeline, and registry scope.
Polyaxon scheduling
Queues, priorities, resources, concurrency, approvals, and Kubernetes settings.
ClearML WebApp
Experiment, model, dataset, pipeline, endpoint, orchestration, and resource-management surfaces.
ClearML workers and queues
Agent environment reconstruction, queues, priority modes, GPUs, Docker, and services mode.
ClearML Pipelines
Controller tasks, Python definitions, remote queues, dependencies, and execution modes.
ClearML Model Registry
Model versions, provenance, lineage, comparison, publishing, and automation.
ClearML Kubernetes agents
Per-queue pod templates, resource settings, and Kubernetes task execution.
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.