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Machine Learning · head to head

AWS SageMaker vs ClearML

AWS SageMaker logo

AWS SageMaker

Machine Learning

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite
ClearML logo

ClearML

Machine Learning

Open-source MLOps platform for experiment tracking and orchestration

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; ClearML broad scope means more to learn and more to run than a focused tracking tool
  • They diverge on capability: AWS SageMaker covers Jupyter notebooks, ClearML covers Experiment tracking.

Where they differ

Only the attributes on which AWS SageMaker and ClearML actually diverge.

Attributes where AWS SageMaker and ClearML differ
AttributeAWS SageMakerClearML
Pricing modelUnknownOpen-source self-hosted, with paid hosted and enterprise tiers
PlatformsWebLinux, macOS, Windows, Docker, Kubernetes
Founded2006Unknown

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in AWS SageMaker

  • Jupyter notebooks
  • Built-in algorithms
  • Automatic model tuning
  • One-click deployment
  • Model monitoring
  • S3
  • Lambda
  • Step Functions

Only in ClearML

  • Experiment tracking
  • Remote execution
  • Data versioning
  • Pipelines

What people use each for

The jobs each tool is most often brought in to do.

AWS SageMaker

  • Machine learningnot ClearML
  • Data analysisnot ClearML
  • Model trainingnot ClearML
  • Predictive analyticsnot ClearML

ClearML

  • Tracking experiments across a team so results are reproduciblenot AWS SageMaker
  • Moving training from laptops to shared GPU hardware without repackagingnot AWS SageMaker
  • Versioning datasets alongside the experiments that consumed themnot AWS SageMaker

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

AWS SageMaker

  • Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
  • Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
  • Does not include native job scheduling, requiring Lambda or EventBridge integration

ClearML

  • Broad scope means more to learn and more to run than a focused tracking tool
  • Self-hosting the server is real infrastructure — database, file storage and web server
  • Documentation quality is uneven across the newer parts of the platform
  • Smaller community than the most popular tracking tools, so fewer worked examples exist

Pricing, plan by plan

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

ClearML

Free
  • Open sourceFree
    • Experiment tracking
    • Pipelines
    • Self-hosted server

Which should you pick?

Choose AWS SageMaker if

  • You need jupyter notebooks.
  • You want to start without paying.
  • You also want built-in algorithms.
AWS SageMaker review

Choose ClearML if

  • You need experiment tracking.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker, Kubernetes.
  • You also want remote execution.
ClearML review

Questions people ask

Is AWS SageMaker or ClearML better?
Neither clearly leads. AWS SageMaker starts at Free and ClearML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, AWS SageMaker or ClearML?
AWS SageMaker starts at Free and ClearML at Free.
Does AWS SageMaker or ClearML run on more platforms?
AWS SageMaker runs on Web. ClearML runs on Linux, macOS, Windows, Docker, Kubernetes.
Can I use AWS SageMaker for free?
Both have a free tier, so you can try either at no cost before committing.
What is AWS SageMaker best used for?
AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what ClearML is typically brought in for.
What can AWS SageMaker do that ClearML cannot?
AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines.

Answered from the vendors’ own pages

AWS SageMaker: What is AWS SageMaker used for?

AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.

Source
ClearML: Is ClearML free?

The open-source version is free and self-hostable. Hosted and enterprise tiers are paid.

AWS SageMaker: How is AWS SageMaker priced?

SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.

Source
ClearML: How much code does tracking require?

Very little — adding a couple of lines to an existing training script captures parameters, metrics and environment automatically.

AWS SageMaker: Does AWS SageMaker have a free tier?

Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.

Source
ClearML: Does ClearML replace MLflow?

It covers MLflow’s tracking and adds orchestration, remote execution and data versioning. Whether that breadth is an advantage or extra weight depends on whether you need the rest.

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