ClearML vs Amazon SageMaker Experiments

ClearML8.8Visit Amazon SageMaker Experiments8.6Visit

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

vs

Amazon SageMaker Experiments #4 in ML Experiment Tracking Software 8.6/10 Paid ✓ 4 of 5 features Visit AWS

• ClearML fits best

  Teams wanting broad, affordable experiment management

• Amazon SageMaker Experiments fits best

  AWS-native ML teams

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Side by side

Feature	ClearML 8.8/10 Visit ↗	Amazon SageMaker Experiments 8.6/10 Visit ↗
At a glance
Editor score	8.8	8.6
Ranking	#2 in ML Experiment Tracking Software	#4 in ML Experiment Tracking Software
Best for	Teams wanting broad, affordable experiment management	AWS-native ML teams
Pricing model	Free plan + paid	Paid
Starting price	$15/user/mo	—
Free plan	✓ (best)	—
Free trial	—	—
Deployment	Cloud, Self-hosted	Cloud
Platforms	Web	Web
Support	Email, Community, Docs	Docs
Compliance	GDPR, SSO/SAML	SOC 2, ISO 27001, GDPR, PCI DSS
Integrations	12 integrations	5 integrations
Built for	Solo, Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features ClearML 1/5 · Amazon SageMaker Experiments 4/5
Run comparison	Not checked yet	✓ (best)
Artifact tracking	Not checked yet	✓ (best)
Dataset versioning	Not checked yet	Not checked yet
Model registry	✓	✓
API and SDK access	Not checked yet	✓ (best)
Specs
Deployment options	Not checked yet	Cloud
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 parameters, metrics, configurations, results, and artifacts Compares experiment runs with metrics and charts Connects MLflow, S3, registries, pipelines, APIs, and SDKs
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	No free plan Compute and storage are billed separately Best suited to teams already centered on AWS services
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 →	Amazon SageMaker Experiments helps data scientists and developers organize, track, analyze, and compare machine learning experiments and training runs. It supports inputs, parameters, configurations, metrics, results, and output artifacts,… 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

• Amazon SageMaker Experiments — where it wins

  • Tracks parameters, metrics, configurations, results, and artifacts
  • Compares experiment runs with metrics and charts
  • Connects MLflow, S3, registries, pipelines, APIs, and SDKs

  Where it doesn't

  • No free plan
  • Compute and storage are billed separately
  • Best suited to teams already centered on AWS services

• ClearML8.8/10 · Free plan

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

  Visit ClearMLFull verdict →
• Amazon SageMaker Experiments8.6/10 · Paid

  A strong fit for AWS-native teams that need tracking, comparison, artifacts, and MLflow.

  Visit AWSFull verdict →