ClearML vs Kubeflow

ClearML

Agents

Kubeflow

Agents

Tagline

ClearML

End-to-end MLOps and GenAI platform with open-source experiment tracking and enterprise GPU orchestration.

Kubeflow

Open-source toolkit for running the full ML lifecycle on Kubernetes.

Pricing

ClearML

Freemium· Community: $0 · Pro: $15 Per User/Month + Usage · Scale: Custom Quote · Enterprise: Request a Quote

Kubeflow

Free· Free and open source; commercial distributions and managed offerings priced separately by vendors

Lowest paid tier

ClearML

$15 Per User/Month + Usage · Pro

captured 2026-08-10

Kubeflow

—

Free trial

ClearML

Yes

Kubeflow

Yes

API

ClearML

Yes

Kubeflow

Yes

Platforms

ClearML

api

Kubeflow

api

Open source

ClearML

Yes

Kubeflow

Yes

Model used

ClearML

Model-agnostic

Kubeflow

Multi-framework (PyTorch, JAX, XGBoost, TensorFlow)

Best for

ClearML

Pick ClearML if you need an open-source MLOps backbone that also handles GPU scheduling and LLM serving for an enterprise or on-prem GPU fleet.

Kubeflow

Pick Kubeflow if you're a platform team building an internal, multi-tenant ML platform on Kubernetes and want CNCF-grade, vendor-neutral building blocks.

Not for

ClearML

Skip it if you just want lightweight experiment logging or a single-user notebook tracker - MLflow or W&B will be lower friction.

Kubeflow

Skip it if you're a solo practitioner or small team without Kubernetes ops capacity, a managed service like Vertex AI or SageMaker will save you months.

Editorial score

ClearML

8.3 / 10

Kubeflow

7.3 / 10

Use cases

ClearML

experiment-trackinggpu-orchestrationmlopsllm-deploymentmodel-registrydata-versioning

Kubeflow

ml-pipelinesdistributed-traininghyperparameter-tuningmodel-registryllm-fine-tuningnotebooks

Pros

ClearML

• Open-source core with permissive self-hosting
• Bundles tracking, orchestration, and GenAI serving in one stack
• Strong fractional-GPU and multi-tenant scheduling for shared clusters
• Vendor-neutral across clouds, on-prem, and silicon
• Active enterprise adoption in regulated industries

Kubeflow

• CNCF-graduated, vendor-neutral, no lock-in to a single cloud
• Covers the full lifecycle: notebooks, pipelines, training, tuning, registry, serving
• Distributed LLM fine-tuning across PyTorch, JAX, XGBoost out of the box
• Huge ecosystem: 33K+ GitHub stars, 3K contributors, mature operator pattern
• Composable, adopt only the subprojects you actually need

Cons

ClearML

• Pricing for enterprise tiers is opaque
• Heavier to deploy than logging-only alternatives
• UI and docs assume MLOps fluency
• GenAI App Engine is newer and less mature than the tracking core

Kubeflow

• Steep operational learning curve, you need real Kubernetes expertise
• Subprojects ship on different cadences, version-matrix headaches are common
• No hosted SaaS, install and upgrade pain falls on your platform team
• Overkill for solo researchers or small teams without a cluster

Website

ClearML

clear.ml

Kubeflow

kubeflow.org

Editorial score: rule-based, 0–10, from AI-assisted profile inputs (see /methodology) — not a user rating; “—” means unscored. “Not listed” means we have no record of it, not that it is absent. GitHub figures and prices carry the date they were checked or captured; prices are shown as published, unconverted.