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Machine Learning · head to head

Azure Machine Learning vs Domo

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
Domo logo

Domo

Business Intelligence

Business cloud for modern enterprises

From
$30000/year
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.; Domo pricing is not published; contracts start around $30,000 per year minimum, making budget planning difficult without a sales conversation
  • They diverge on capability: Azure Machine Learning covers Workspace, Domo covers 1000+ Connectors.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Azure Machine Learning and Domo differ
AttributeAzure Machine LearningDomo
Starting priceFree$30000/year
Pricing modelusage-basedUnknown
Free tierYesNo
PlatformsAzure CloudWeb, Mobile, Api
CategoryMachine LearningBusiness Intelligence
Founded19752010

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 Domo

  • 1000+ Connectors
  • Real-time Data
  • Mobile BI
  • Collaboration
  • App Development
  • Salesforce
  • Google Analytics
  • Facebook

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

Domo

  • Self-service analyticsnot Azure Machine Learning
  • Data explorationnot Azure Machine Learning
  • Ad-hoc reportingnot Azure Machine Learning
  • Collaborative analysisnot Azure Machine Learning
  • Embedded analyticsnot 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.

Domo

  • Pricing is not published; contracts start around $30,000 per year minimum, making budget planning difficult without a sales conversation
  • Visualization customization is limited compared to specialized tools like Tableau, with rigid chart types and restricted pixel-level dashboard layouts
  • Version control and merge options for dataflows are very limited, making multi-developer projects prone to conflicts and overwrites
  • Workflows cannot be edited once deployed; any changes require rebuilding from scratch
  • Semantic layer lacks code-based governance, with metric definitions scattered inside individual cards rather than in a centralized governed location

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

Domo

$30000/year

No published plan breakdown. See the Domo review.

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 Domo if

  • You need 1000+ connectors.
  • You work on Web, Mobile, Api.
  • You also want real-time data.
Domo review

Questions people ask

Is Azure Machine Learning or Domo better?
Neither clearly leads. Azure Machine Learning starts at Free and Domo at $30000/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Azure Machine Learning or Domo?
Azure Machine Learning has a free tier; the other does not. Paid plans start at Free for Azure Machine Learning and $30000/year for Domo.
Does Azure Machine Learning or Domo run on more platforms?
Azure Machine Learning runs on Azure Cloud. Domo runs on Web, Mobile, Api.
Can I use Azure Machine Learning for free?
Yes. Azure Machine Learning has a free tier, so you can try it without paying. Domo starts at $30000/year.
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 Domo is typically brought in for.
What can Azure Machine Learning do that Domo cannot?
Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. Domo covers 1000+ Connectors, Real-time Data, Mobile BI, Collaboration.

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.

Domo: Does Domo offer a free tier or trial?

Domo does not publish pricing on its website and does not offer a standard free tier. The platform uses a consumption-based credit model with minimum viable deployments starting around $30,000 per year. A free trial may be available upon request from the sales team.

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.

Domo: What data sources can Domo connect to?

Domo connects to over 1,000 pre-built connectors covering cloud applications, databases, advertising platforms, file services, spreadsheets, enterprise systems, and data warehouses. Custom integrations are possible via API.

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.

Domo: Can I self-host Domo or is it cloud-only?

Domo is a fully cloud-native, SaaS platform with no self-hosted option available. All data and applications run on Domo's cloud infrastructure.

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.

Domo: What does the credit-based pricing model mean?

Domo charges credits based on data consumption and platform activity. One credit roughly equals processing one million rows of data, though actual burn rate varies with workflows. Users purchase credit packages providing team access with unlimited user seats; only activity consumes credits, not dashboards or team size.

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.

Domo: Does Domo include AI features and what do they cost?

Domo AI features are free as part of your contract, including DomoGPT for AI chat queries. Premium AI capabilities are available through Domo AI Pro, which uses consumption-based pricing on a per-use basis.

Source
Domo: Can multiple teams collaborate on the same dashboard in Domo?

Yes, Domo supports team collaboration on shared dashboards and datasets. However, version control and merge capabilities for dataflows are limited, which can cause conflicts when multiple developers work on the same project.

Source
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