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Business Intelligence · head to head

Anaplan vs Azure Machine Learning

Anaplan logo

Anaplan

Business Intelligence

Connected planning platform with an in-memory calculation engine for large multidimensional models

From
On request
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

  • Only Azure Machine Learning has a free tier, so it costs nothing to try first.
  • Each has a real cost: Anaplan workspace is licensed by memory consumed, so a model that grows as the business adds SKUs, regions or scenarios generates a bill increase without a single new user being added, and teams end up optimising models for licence cost rather than clarity.; 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: Anaplan covers Hyperblock calculation engine, Azure Machine Learning covers Workspace.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Anaplan and Azure Machine Learning differ
AttributeAnaplanAzure Machine Learning
Starting priceOn requestFree
Pricing modelquoteusage-based
Free tierNoYes
PlatformsWeb, iOSAzure Cloud
CategoryBusiness IntelligenceMachine Learning
FoundedUnknown1975

Identical on both: 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 Anaplan

  • Hyperblock calculation engine
  • Connected planning
  • Scenario and versioning
  • Model builder
  • Anaplan PlanIQ
  • Workflow and approvals
  • Application lifecycle management
  • Data integration

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.

Anaplan

  • Sales territory and quota planning across thousands of reps where a change to segmentation must reflow quota immediatelynot Azure Machine Learning
  • Demand and supply planning at SKU and location level for a manufacturer with tens of thousands of itemsnot Azure Machine Learning
  • Workforce planning that ties headcount, cost and capacity to a revenue plan across dozens of business unitsnot Azure Machine Learning
  • Replacing a spreadsheet estate where the master planning model has become too large and too fragile for Excel to open reliablynot 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 Anaplan
  • Training that needs to burst onto a GPU cluster occasionally without buying hardware, with the cluster scaling back to zero afterwardsnot Anaplan
  • Regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based accessnot Anaplan
  • Teams already using MLflow who want the tracking interface they know backed by a managed service and enterprise identitynot Anaplan

Where each one falls short

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

Anaplan

  • Workspace is licensed by memory consumed, so a model that grows as the business adds SKUs, regions or scenarios generates a bill increase without a single new user being added, and teams end up optimising models for licence cost rather than clarity.
  • Model building requires certified Anaplan modellers using a proprietary formula language, and the labour market for that skill is small, so most customers stay dependent on a systems integrator long after go-live.
  • Thoma Bravo took the company private in 2022 in a $10.7bn deal, and customers have since reported firmer renewal terms; a private-equity owner optimising for cash flow is a real factor in a multi-year planning contract.
  • Native reporting and visualisation are weak for anything beyond planning grids, so most customers push data out to Power BI or Tableau for executive reporting, adding another tool and another latency point.
  • Implementations are long. A connected planning programme across finance and supply chain routinely runs six to eighteen months before the first production plan, which is difficult to justify when the business wants a forecast this quarter.

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

Anaplan

On request
  • Anaplan$undefined/year
    • Licensed by user tier and by workspace capacity
    • Workspace charged on memory consumed by models, independent of user count
    • Multi-year enterprise agreements are the norm

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 Anaplan if

  • You need hyperblock calculation engine.
  • You work on Web, iOS.
  • You also want connected planning.
Anaplan 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 Anaplan or Azure Machine Learning better?
Neither clearly leads. Anaplan starts at On request and Azure Machine Learning at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Anaplan or Azure Machine Learning?
Azure Machine Learning has a free tier; the other does not. Paid plans start at On request for Anaplan and Free for Azure Machine Learning.
Does Anaplan or Azure Machine Learning run on more platforms?
Anaplan runs on Web, iOS. Azure Machine Learning runs on Azure Cloud.
Can I use Azure Machine Learning for free?
Yes. Azure Machine Learning has a free tier, so you can try it without paying. Anaplan starts at On request.
What is Anaplan best used for?
Anaplan is most often used for sales territory and quota planning across thousands of reps where a change to segmentation must reflow quota immediately, demand and supply planning at sku and location level for a manufacturer with tens of thousands of items, workforce planning that ties headcount, cost and capacity to a revenue plan across dozens of business units, replacing a spreadsheet estate where the master planning model has become too large and too fragile for excel to open reliably. Of those, sales territory and quota planning across thousands of reps where a change to segmentation must reflow quota immediately and demand and supply planning at sku and location level for a manufacturer with tens of thousands of items are not what Azure Machine Learning is typically brought in for.
What can Anaplan do that Azure Machine Learning cannot?
Anaplan covers Hyperblock calculation engine, Connected planning, Scenario and versioning, Model builder. Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry.

Answered from the vendors’ own pages

Anaplan: Why is Anaplan expensive even when user counts are low?

Because workspace is licensed on the memory your models consume as well as on users. Large models cost money regardless of how many people log in.

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.

Anaplan: Do we need a systems integrator?

Almost always for the first implementation. The proprietary modelling language and the scale of typical models make an experienced partner or an internal certified team effectively mandatory.

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.

Anaplan: Who owns Anaplan?

Thoma Bravo, which took it private in 2022 for $10.7bn.

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.

Anaplan: Can it replace our BI tool?

No. It is a planning and calculation platform; most customers still export to Power BI or Tableau for reporting and dashboards.

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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