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Machine Learning · head to head

ClearML vs RapidMiner

ClearML logo

ClearML

Machine Learning

Open-source MLOps platform for experiment tracking and orchestration

From
Free
Rated
-
Full reviewWebsite
RapidMiner logo

RapidMiner

Machine Learning

Visual workflow data science platform, now sold by Altair as AI Studio

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; RapidMiner processes are stored as the product's own XML, so they cannot be meaningfully diffed, reviewed in a pull request or executed anywhere else, and a team's accumulated work is not portable in any practical sense.
  • They diverge on capability: ClearML covers Experiment tracking, RapidMiner covers Visual process canvas.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which ClearML and RapidMiner actually diverge.

Attributes where ClearML and RapidMiner differ
AttributeClearMLRapidMiner
Pricing modelOpen-source self-hosted, with paid hosted and enterprise tiersfreemium
PlatformsLinux, macOS, Windows, Docker, KubernetesLinux, Mac, Windows, Web
FoundedUnknown2007

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 RapidMiner

  • Visual process canvas
  • Operator library
  • Automatic modelling
  • Python and R operators
  • Validation operators
  • Text and time series extensions
  • AI Hub server
  • Altair portfolio integration

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 RapidMiner
  • Moving training from laptops to shared GPU hardware without repackagingnot RapidMiner
  • Versioning datasets alongside the experiments that consumed themnot RapidMiner

RapidMiner

  • Modelling work in an engineering organisation where the analysis must be reviewable by people who do not codenot ClearML
  • Teaching data science concepts, where seeing the validation split as a visible connection is more instructive than reading a function callnot ClearML
  • Companies already holding Altair licences, where adding this draws on units already purchased rather than a new procurementnot ClearML
  • Business analysts building predictive workflows without a data science team to hand the problem tonot 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

RapidMiner

  • Processes are stored as the product's own XML, so they cannot be meaningfully diffed, reviewed in a pull request or executed anywhere else, and a team's accumulated work is not portable in any practical sense.
  • The operator library is the ceiling, and anything beyond it means dropping into an embedded Python or R operator, at which point the code sits inside a visual container that provides none of the version control, testing or debugging a normal repository would.
  • Two changes of ownership in three years, Altair in 2022 and Siemens thereafter, have already moved the product's name, packaging and licensing, so a buyer is committing to a roadmap decided inside a much larger engineering software business.
  • Licensing draws on Altair's shared units pool, so running heavy modelling work consumes capacity that other teams in the organisation were relying on for different products, which makes cost attribution and capacity planning awkward.
  • Scheduling and deployment require AI Hub as a separate server product to install, license and operate, so a model built on the desktop is not in production until another purchase and another installation have been completed.

Pricing, plan by plan

ClearML

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

RapidMiner

Free
  • FreeFree
    • 10,000 data rows
    • 1 logical processor
  • ProfessionalFree
    • Unlimited data
    • Full features
    • Support

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 RapidMiner if

  • You need visual process canvas.
  • You want to start without paying.
  • You work on Linux, Mac, Windows, Web.
  • You also want operator library.
RapidMiner review

Questions people ask

Is ClearML or RapidMiner better?
Neither clearly leads. ClearML starts at Free and RapidMiner at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, ClearML or RapidMiner?
ClearML starts at Free and RapidMiner at Free.
Does ClearML or RapidMiner run on more platforms?
ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. RapidMiner runs on Linux, Mac, Windows, Web.
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 RapidMiner is typically brought in for.
What can ClearML do that RapidMiner cannot?
ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. RapidMiner covers Visual process canvas, Operator library, Automatic modelling, Python and R operators.

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.

RapidMiner: Is it still called RapidMiner?

The desktop product is now Altair AI Studio and the server is Altair AI Hub. The RapidMiner name persists in documentation, community material and most search results, which makes finding current information harder than it should be.

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.

RapidMiner: Is there a free version?

Altair has offered free and academic editions with usage limits, but the terms have moved with each ownership change, so check what is currently on offer rather than relying on what the free tier allowed a few years ago.

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.

RapidMiner: Do I need to write code?

No, which is the point of it. You will write some once you hit the edge of the operator library, and at that stage the tool works against you rather than for you.

RapidMiner: Can I put a model into production?

Through AI Hub, which is a separate licensed server. The desktop tool builds and validates; it does not schedule or serve.

RapidMiner: How does licensing work?

Through Altair's units model, where a pool of purchased units is drawn on by whichever Altair products your organisation runs, rather than a per-seat licence specific to this product.

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