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Machine Learning · head to head

Azure Machine Learning vs Weka

Azure Machine Learning logo

Azure Machine Learning

Machine Learning

Microsoft's managed platform for training, tracking and deploying models on Azure

From
Free
Rated
-
Full reviewWebsite
Weka logo

Weka

Machine Learning

Collection of machine learning algorithms

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Azure Machine Learning managed online endpoints are billed per underlying virtual machine for as long as the deployment exists, with no scale to zero, so a model answering a handful of requests a day costs the same as one answering thousands.; Weka the package management system needs an internet connection to download and install packages, so an air-gapped install gets only the core distribution
  • They diverge on capability: Azure Machine Learning covers Workspace, Weka covers Classification.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Azure Machine Learning and Weka actually diverge.

Attributes where Azure Machine Learning and Weka differ
AttributeAzure Machine LearningWeka
Pricing modelusage-basedopen-source
PlatformsAzure CloudLinux, Mac, Windows
Founded19751993

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 Azure Machine Learning

  • Workspace
  • Compute clusters
  • MLflow-compatible tracking
  • Model registry
  • Managed online endpoints
  • Batch endpoints
  • Automated machine learning
  • Pipelines

Only in Weka

  • Classification
  • Regression
  • Clustering
  • Association rules
  • Feature selection
  • Java
  • R
  • Python

What people use each for

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

Azure Machine Learning

  • Enterprises standardised on Azure where using a different cloud for machine learning would mean a fresh security and compliance reviewnot Weka
  • Training that needs to burst onto a GPU cluster occasionally without buying hardware, with the cluster scaling back to zero afterwardsnot Weka
  • Regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based accessnot Weka
  • Teams already using MLflow who want the tracking interface they know backed by a managed service and enterprise identitynot Weka

Weka

  • Teaching and exploring classic machine learning algorithms through a GUInot Azure Machine Learning
  • Running data mining experiments and preprocessing without writing codenot Azure Machine Learning

Where each one falls short

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

Azure Machine Learning

  • Managed online endpoints are billed per underlying virtual machine for as long as the deployment exists, with no scale to zero, so a model answering a handful of requests a day costs the same as one answering thousands.
  • GPU capacity is governed by per-region, per-family quota that must be requested and approved, so a training plan can be blocked by an administrative ticket rather than by budget, and the newest accelerators are often unavailable in the region your data is required to stay in.
  • The v2 Python SDK and command line use a different object model from v1 and code, pipelines and examples written for v1 do not port mechanically, which has left teams maintaining two ways of doing the same thing and searching documentation that mixes both.
  • The workspace binds storage, key vault, container registry and compute together, so recreating or moving one is not a light operation, and configuring it properly with private endpoints and a managed virtual network is a multi-day job for somebody who already knows Azure networking.
  • Experiment history, registered models, environments, endpoints and pipeline definitions live inside the workspace, and although the tracking interface is MLflow-compatible, moving the accumulated lineage and orchestration elsewhere is a rebuild, so the cost of leaving grows every month the team uses it.

Weka

  • The package management system needs an internet connection to download and install packages, so an air-gapped install gets only the core distribution
  • Weka is split into a stable 3.8 branch that receives only bug fixes and compatibility-safe upgrades and a 3.9 development branch that may receive features that break compatibility
  • Weka requires a 64-bit Java VM; the bundled installers ship Bellsoft OpenJDK 25 per platform and architecture

Pricing, plan by plan

Azure Machine Learning

Free
  • Free TierFree
    • Limited compute
    • Basic features
  • Pay-as-you-go$0.05/hour
    • Full platform
    • All compute options
    • Enterprise features

Weka

Free
  • Open SourceFree
    • All ML algorithms
    • GUI and CLI
    • Java API

Which should you pick?

Choose Azure Machine Learning if

  • You need workspace.
  • You want to start without paying.
  • You work on Azure Cloud.
  • You also want compute clusters.
Azure Machine Learning review

Choose Weka if

  • You need classification.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want regression.
Weka review

Questions people ask

Is Azure Machine Learning or Weka better?
Neither clearly leads. Azure Machine Learning starts at Free and Weka at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Azure Machine Learning or Weka?
Azure Machine Learning starts at Free and Weka at Free.
Does Azure Machine Learning or Weka run on more platforms?
Azure Machine Learning runs on Azure Cloud. Weka runs on Linux, Mac, Windows.
Can I use Azure Machine Learning for free?
Both have a free tier, so you can try either at no cost before committing.
What is Azure Machine Learning best used for?
Azure Machine Learning is most often used for enterprises standardised on azure where using a different cloud for machine learning would mean a fresh security and compliance review, training that needs to burst onto a gpu cluster occasionally without buying hardware, with the cluster scaling back to zero afterwards, regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based access, teams already using mlflow who want the tracking interface they know backed by a managed service and enterprise identity. Of those, enterprises standardised on azure where using a different cloud for machine learning would mean a fresh security and compliance review and training that needs to burst onto a gpu cluster occasionally without buying hardware, with the cluster scaling back to zero afterwards are not what Weka is typically brought in for.
What can Azure Machine Learning do that Weka cannot?
Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. Weka covers Classification, Regression, Clustering, Association rules.

Answered from the vendors’ own pages

Azure Machine Learning: Is there a charge for the workspace itself?

No charge for the workspace resource. You pay for the compute it runs, the storage it uses, the container registry, key vault and any endpoints left running, which is where essentially the whole bill comes from.

Weka: What is the cost of Weka software?

Weka is provided at no cost as open-source software released under the GNU General Public License, making it freely available for download and use.

Source
Azure Machine Learning: Does it work with MLflow?

Yes. The tracking interface is MLflow-compatible, so existing logging code generally works unchanged, and that compatibility is the least locked-in part of the platform.

Weka: Are there commercial licensing options available?

Yes, the project offers information about commercial licenses for organizations requiring non-GPL terms, which can be found in their commercial applications documentation.

Source
Azure Machine Learning: What is the difference between SDK v1 and v2?

A different object model and a different way of expressing jobs, components and endpoints. v2 is the current one. v1 code does not translate mechanically and a lot of material found online still assumes v1, which is a common source of wasted time.

Weka: What support resources are available to users?

Multiple support avenues exist including comprehensive documentation, frequently asked questions, dedicated help resources, and access to courses for learning the platform.

Source
Azure Machine Learning: Do endpoints scale to zero?

Managed online endpoints do not; they hold their virtual machines. Batch endpoints only consume compute while a job runs, so intermittent workloads are much cheaper served as batch where the use case allows it.

Weka: Is source code access provided?

Yes, developers have full access to source code through the Git repository, along with development documentation and code credits for contributors.

Source
Azure Machine Learning: Do I need an ML engineer to run it?

For the data science work, not necessarily. For the workspace itself, yes, somebody has to understand Azure identity, networking, quota and cost management, and on teams without that person the platform becomes the bottleneck rather than the model.

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