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Machine Learning · head to head

ClearML vs Hugging Face

ClearML logo

ClearML

Machine Learning

Open-source MLOps platform for experiment tracking and orchestration

From
Free
Rated
-
Full reviewWebsite
Hugging Face logo

Hugging Face

Machine Learning

The AI community building the future

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: ClearML broad scope means more to learn and more to run than a focused tracking tool; Hugging Face model discovery across 3 million models lacks robust filtering and sorting by quality metrics
  • They diverge on capability: ClearML covers Experiment tracking, Hugging Face covers Model hub.

Where they differ

Only the attributes on which ClearML and Hugging Face actually diverge.

Attributes where ClearML and Hugging Face differ
AttributeClearMLHugging Face
Pricing modelOpen-source self-hosted, with paid hosted and enterprise tiersUnknown
PlatformsLinux, macOS, Windows, Docker, KubernetesWeb, API
FoundedUnknown2016

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 ClearML

  • Experiment tracking
  • Remote execution
  • Data versioning
  • Pipelines

Only in Hugging Face

  • Model hub
  • Datasets
  • Spaces
  • Transformers library
  • GitHub
  • Cloud providers
  • MLOps tools
  • Web support

What people use each for

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

ClearML

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

Hugging Face

  • ai tools managementnot ClearML
  • Workflow automationnot ClearML
  • Reportingnot ClearML

Where each one falls short

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

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

Hugging Face

  • Model discovery across 3 million models lacks robust filtering and sorting by quality metrics
  • Community-driven content means variable model quality and documentation
  • Private models and datasets require Pro subscription
  • Enterprise support and SLAs require custom arrangements

Pricing, plan by plan

ClearML

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

Hugging Face

Free

No published plan breakdown. See the Hugging Face review.

Which should you pick?

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

Choose Hugging Face if

  • You need model hub.
  • You want to start without paying.
  • You work on Web, API.
  • You also want datasets.
Hugging Face review

Questions people ask

Is ClearML or Hugging Face better?
Neither clearly leads. ClearML starts at Free and Hugging Face at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, ClearML or Hugging Face?
ClearML starts at Free and Hugging Face at Free.
Does ClearML or Hugging Face run on more platforms?
ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. Hugging Face runs on Web, API.
Can I use ClearML for free?
Both have a free tier, so you can try either at no cost before committing.
What is ClearML best used for?
ClearML is most often used for tracking experiments across a team so results are reproducible, moving training from laptops to shared gpu hardware without repackaging, versioning datasets alongside the experiments that consumed them. Of those, tracking experiments across a team so results are reproducible and moving training from laptops to shared gpu hardware without repackaging are not what Hugging Face is typically brought in for.
What can ClearML do that Hugging Face cannot?
ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. Hugging Face covers Model hub, Datasets, Spaces, Transformers library.

Answered from the vendors’ own pages

ClearML: Is ClearML free?

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

Hugging Face: Is Hugging Face free to use?

Yes. Hugging Face allows users to host and collaborate on unlimited public models, datasets, and applications at no cost. Models can be accessed and used freely from the Hub.

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.

Hugging Face: How many models are available on Hugging Face?

Hugging Face Hub currently hosts nearly 3 million machine learning models across various tasks including text generation, image processing, and video generation.

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.

Hugging Face: What is the Hugging Face Inference API?

Hugging Face provides access to 45,000+ models from leading AI providers through a single unified API with no service fees, simplifying access to diverse models.

Source
Hugging Face: What content types does Hugging Face support?

Hugging Face supports text, image, video, audio, and 3D content models, allowing collaboration across multiple modalities and use cases.

Source
Hugging Face: What is the transformers library?

Transformers is a Hugging Face library built for natural language processing applications, providing pre-built models and utilities for NLP tasks.

Source
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