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Education · head to head

360Learning vs Azure Machine Learning

360Learning logo

360Learning

Education

Collaborative learning that transforms L&D

From
Free
Rated
-
Full reviewWebsite
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

The short version

  • Each has a real cost: 360Learning the published Team plan at $8 per user per month covers up to 100 users; beyond that pricing is custom; 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.
  • They diverge on capability: 360Learning covers Collaborative authoring, Azure Machine Learning covers Workspace.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which 360Learning and Azure Machine Learning actually diverge.

Attributes where 360Learning and Azure Machine Learning differ
Attribute360LearningAzure Machine Learning
Pricing modelsubscriptionusage-based
PlatformsWeb, IOS, Android, APIAzure Cloud
CategoryEducationMachine Learning
Founded20101975

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

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 360Learning

  • Collaborative authoring
  • AI-powered recommendations
  • Social learning
  • Assessments
  • Mobile learning
  • Analytics
  • Integrations
  • Gamification

Only in Azure Machine Learning

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

What people use each for

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

360Learning

  • Collaborative course authoring by internal subject matter expertsnot Azure Machine Learning
  • Onboarding and compliance training deliverynot Azure Machine Learning
  • Upskilling programmes tracked across a workforcenot Azure Machine Learning
  • Customer and partner trainingnot Azure Machine Learning

Azure Machine Learning

  • Enterprises standardised on Azure where using a different cloud for machine learning would mean a fresh security and compliance reviewnot 360Learning
  • Training that needs to burst onto a GPU cluster occasionally without buying hardware, with the cluster scaling back to zero afterwardsnot 360Learning
  • Regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based accessnot 360Learning
  • Teams already using MLflow who want the tracking interface they know backed by a managed service and enterprise identitynot 360Learning

Where each one falls short

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

360Learning

  • The published Team plan at $8 per user per month covers up to 100 users; beyond that pricing is custom
  • Business and Enterprise pricing is not published
  • Priority SLA, dedicated technical support and premium onboarding are Enterprise only
  • Business and Enterprise plans are typically annual contracts rather than monthly

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.

Pricing, plan by plan

360Learning

Free
  • Team$8/month
    • Up to 100 users
    • No minimum number of users required
    • Pay only for active users
  • Business$null/custom
    • Flexible user models: registered and monthly active users
    • Growing organizations and L&D teams ready to scale
    • Annual contracts generally offered
  • Enterprise$null/custom
    • Flexible user models: registered and monthly active users
    • Large organizations seeking enterprise-wide L&D transformation
    • Annual contracts generally offered

Azure Machine Learning

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

Which should you pick?

Choose 360Learning if

  • You need collaborative authoring.
  • You want to start without paying.
  • You work on Web, IOS, Android, API.
  • You also want ai-powered recommendations.
360Learning review

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

Questions people ask

Is 360Learning or Azure Machine Learning better?
Neither clearly leads. 360Learning starts at Free and Azure Machine Learning at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, 360Learning or Azure Machine Learning?
360Learning starts at Free and Azure Machine Learning at Free.
Does 360Learning or Azure Machine Learning run on more platforms?
360Learning runs on Web, IOS, Android, API. Azure Machine Learning runs on Azure Cloud.
Can I use 360Learning for free?
Both have a free tier, so you can try either at no cost before committing.
What is 360Learning best used for?
360Learning is most often used for collaborative course authoring by internal subject matter experts, onboarding and compliance training delivery, upskilling programmes tracked across a workforce, customer and partner training. Of those, collaborative course authoring by internal subject matter experts and onboarding and compliance training delivery are not what Azure Machine Learning is typically brought in for.
What can 360Learning do that Azure Machine Learning cannot?
360Learning covers Collaborative authoring, AI-powered recommendations, Social learning, Assessments. Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry.

Answered from the vendors’ own pages

360Learning: Does 360Learning offer a free trial before committing to a paid plan?

Yes, a 30-day free trial is available with no credit card required. Customers can easily upgrade to a paid plan or add more users at any time without disrupting their setup.

Source
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.

360Learning: Can I scale up my user count on the Team plan, or do I need to upgrade to Business?

The Team plan accommodates up to 100 users with no minimum user requirement. You pay only for active users on the platform. To exceed 100 users, you would need to contact sales for a Business or Enterprise plan with custom pricing.

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.

360Learning: What billing flexibility does 360Learning offer for larger organizations?

Business and Enterprise plans use flexible user models that combine both registered and monthly active users. Annual contracts are generally offered for these tiers, with no hidden setup fees.

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.

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.

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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