ClearML vs Valohai

ClearML8.8Visit Valohai8.3Visit

Higher score ClearML #2 in ML Experiment Tracking Software 8.8/10 Free plan Free plan✓ 1 of 5 features Visit ClearML

vs

Valohai #6 in ML Experiment Tracking Software 8.3/10 Pricing on request · 14-day trial Free trial✓ 1 of 5 features Visit Valohai

• ClearML fits best

  Teams wanting broad, affordable experiment management

• Valohai fits best

  Governed multi-cloud enterprise ML teams

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Side by side

Feature	ClearML 8.8/10 Visit ↗	Valohai 8.3/10 Visit ↗
At a glance
Editor score	8.8	8.3
Ranking	#2 in ML Experiment Tracking Software	#6 in ML Experiment Tracking Software
Best for	Teams wanting broad, affordable experiment management	Governed multi-cloud enterprise ML teams
Pricing model	Free plan + paid	Paid
Starting price	$15/user/mo	Pricing on request · 14-day trial
Free plan	✓ (best)	—
Free trial	—	14 days
Deployment	Cloud, Self-hosted	Cloud, Self-hosted
Platforms	Web	Web
Support	Email, Community, Docs	Email, Docs
Compliance	GDPR, SSO/SAML	SOC 2, GDPR, SSO/SAML, 2FA
Integrations	12 integrations	20 integrations
Built for	Solo, Small business, Mid-market, Enterprise	Mid-market, Enterprise
Features ClearML 1/5 · Valohai 1/5
Run comparison	Not checked yet	Not checked yet
Artifact tracking	Not checked yet	Not checked yet
Dataset versioning	Not checked yet	Not checked yet
Model registry	✓	✓
API and SDK access	Not checked yet	Not checked yet
Specs
Deployment options	Not checked yet	Not checked yet
Our review
Pros	Community includes tracking, datasets, models, pipelines, and orchestration Supports web, API, self-hosted, VPC, on-premises, air-gapped, and hybrid deployments Integrates with PyTorch, TensorFlow, scikit-learn, Hugging Face, Optuna, and Hydra	Tracks metrics, logs, parameters, code, data, and reproducibility information Connects registries, pipelines, serving, and data versioning in one platform Supports multi-cloud, Kubernetes, Slurm, on-premises, and distributed training
Cons	Scale and Enterprise use quote-based pricing Pro adds usage-based storage and activity costs Its broad MLOps scope may exceed the needs of tracking-only teams	Pricing requires contacting sales There is no free plan Its wider MLOps scope may exceed tracking-only requirements
Our verdict	ClearML is an open-source AI infrastructure and MLOps platform for data scientists, ML engineers, DevOps teams, and enterprises. Its scope extends from experiment tracking to dataset versioning, model management, pipeline orchestration,… Read the review →	Valohai is an MLOps platform for data scientists, ML engineers, and enterprise ML teams. Its experiment tracking covers metrics, logs, parameters, and reproducibility information, while the wider platform manages code, data, models,… Read the review →

Strengths and trade-offs

• ClearML — where it wins

  • Community includes tracking, datasets, models, pipelines, and orchestration
  • Supports web, API, self-hosted, VPC, on-premises, air-gapped, and hybrid deployments
  • Integrates with PyTorch, TensorFlow, scikit-learn, Hugging Face, Optuna, and Hydra

  Where it doesn't

  • Scale and Enterprise use quote-based pricing
  • Pro adds usage-based storage and activity costs
  • Its broad MLOps scope may exceed the needs of tracking-only teams

• Valohai — where it wins

  • Tracks metrics, logs, parameters, code, data, and reproducibility information
  • Connects registries, pipelines, serving, and data versioning in one platform
  • Supports multi-cloud, Kubernetes, Slurm, on-premises, and distributed training

  Where it doesn't

  • Pricing requires contacting sales
  • There is no free plan
  • Its wider MLOps scope may exceed tracking-only requirements

• ClearML8.8/10 · Free plan

  A broad MLOps platform with experiment tracking, data versioning, orchestration, and serving.

  Visit ClearMLFull verdict →
• Valohai8.3/10 · Pricing on request · 14-day trial

  A broad MLOps platform for teams that need tracking plus production governance.

  Visit ValohaiFull verdict →