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Machine Learning · head to head

Azure Machine Learning vs Dundas BI

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
Dundas BI logo

Dundas BI

Business Intelligence

Flexible business intelligence platform

From
$500/month
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: 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.; Dundas BI the published Embedded BI Package starts from about $4,738.70 USD per month billed annually for 8 core capacity
  • They diverge on capability: Azure Machine Learning covers Workspace, Dundas BI covers White-labeling.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Azure Machine Learning and Dundas BI differ
AttributeAzure Machine LearningDundas BI
Starting priceFree$500/month
Pricing modelusage-basedsubscription
Free tierYesNo
PlatformsAzure CloudWeb, Embedded, Mobile
CategoryMachine LearningBusiness Intelligence
Founded19751992

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 Azure Machine Learning

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

Only in Dundas BI

  • White-labeling
  • Embedded Analytics
  • Data Preparation
  • Custom Visualizations
  • API
  • SQL Server
  • Oracle
  • PostgreSQL

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 Dundas BI
  • Training that needs to burst onto a GPU cluster occasionally without buying hardware, with the cluster scaling back to zero afterwardsnot Dundas BI
  • Regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based accessnot Dundas BI
  • Teams already using MLflow who want the tracking interface they know backed by a managed service and enterprise identitynot Dundas BI

Dundas BI

  • Embedding dashboards and analytics inside another applicationnot Azure Machine Learning
  • Self service business intelligence and ad hoc reportingnot Azure Machine Learning
  • Building custom data visualisations against open BI APIsnot 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.

Dundas BI

  • The published Embedded BI Package starts from about $4,738.70 USD per month billed annually for 8 core capacity
  • That figure reflects a limited time 35 percent promotional discount rather than list price
  • Licensing is by CPU core capacity rather than by user, so cost scales with server hardware
  • Dundas is now part of insightsoftware following acquisition
  • The page states that a pricing plan is worked out together with the vendor rather than published as a rate card

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

Dundas BI

$500/month
  • Professional$500/month
    • Full Platform
    • Embedding
    • Support
  • EnterpriseFree
    • Unlimited Users
    • Multi-tenant
    • Premium Support

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 Dundas BI if

  • You need white-labeling.
  • You work on Web, Embedded, Mobile.
  • You also want embedded analytics.
Dundas BI review

Questions people ask

Is Azure Machine Learning or Dundas BI better?
Neither clearly leads. Azure Machine Learning starts at Free and Dundas BI at $500/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Azure Machine Learning or Dundas BI?
Azure Machine Learning has a free tier; the other does not. Paid plans start at Free for Azure Machine Learning and $500/month for Dundas BI.
Does Azure Machine Learning or Dundas BI run on more platforms?
Azure Machine Learning runs on Azure Cloud. Dundas BI runs on Web, Embedded, Mobile.
Can I use Azure Machine Learning for free?
Yes. Azure Machine Learning has a free tier, so you can try it without paying. Dundas BI starts at $500/month.
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 Dundas BI is typically brought in for.
What can Azure Machine Learning do that Dundas BI cannot?
Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. Dundas BI covers White-labeling, Embedded Analytics, Data Preparation, Custom Visualizations.

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.

Dundas BI: How much does Dundas BI cost?

Dundas BI does not publish specific pricing. The vendor emphasizes 'smarter licensing models to scale appropriately and achieve ROI rapidly' and offers 'custom plans that fit your needs' rather than standardized tiers. Customers must contact Dundas directly for pricing quotes tailored to their enterprise or embedded analytics use case.

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.

Dundas BI: What licensing options does Dundas BI offer?

Dundas BI describes their approach as flexible licensing designed to scale based on deployment needs. Specific licensing models and per-seat or concurrent-user costs are determined through custom quotes.

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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