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Business Intelligence · head to head

Amazon QuickSight vs Azure Machine Learning

Amazon QuickSight logo

Amazon QuickSight

Business Intelligence

Scalable, serverless BI by AWS

From
$3/month
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: Amazon QuickSight reader and Reader Pro roles charged separately at $3 and $20/month; 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: Amazon QuickSight covers SPICE In-memory Engine, Azure Machine Learning covers Workspace.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Amazon QuickSight and Azure Machine Learning differ
AttributeAmazon QuickSightAzure Machine Learning
Starting price$3/monthFree
Pricing modelper-userusage-based
Free tierNoYes
PlatformsAWSAzure Cloud
CategoryBusiness IntelligenceMachine Learning
Founded20061975

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 Amazon QuickSight

  • SPICE In-memory Engine
  • ML Insights
  • Natural Language Queries
  • Embedded Analytics
  • Pay-per-session
  • Redshift
  • S3
  • Athena

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.

Amazon QuickSight

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

Where each one falls short

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

Amazon QuickSight

  • Reader and Reader Pro roles charged separately at $3 and $20/month
  • Author and Author Pro roles charged at $24 and $40/month
  • $250/month infrastructure fee required if Pro users or Q&A enabled
  • SPICE storage charged at $0.38/GB monthly (10 GB included)
  • Pixel-perfect reports start at $500/month for 500 monthly units
  • Alerts charged at $0.05-$0.50 per 1,000 metrics evaluated

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

Amazon QuickSight

$3/month
  • Reader$3/month
    • Dashboard viewing only
  • Reader Pro$20/month
    • Enhanced reader capabilities
  • Author$24/month
    • Dashboard creation and editing
  • Author Pro$40/month
    • Advanced authoring features

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 Amazon QuickSight if

  • You need spice in-memory engine.
  • You work on AWS.
  • You also want ml insights.
Amazon QuickSight 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 Amazon QuickSight or Azure Machine Learning better?
Neither clearly leads. Amazon QuickSight starts at $3/month and Azure Machine Learning at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Amazon QuickSight or Azure Machine Learning?
Azure Machine Learning has a free tier; the other does not. Paid plans start at $3/month for Amazon QuickSight and Free for Azure Machine Learning.
Does Amazon QuickSight or Azure Machine Learning run on more platforms?
Amazon QuickSight runs on AWS. 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. Amazon QuickSight starts at $3/month.
What is Amazon QuickSight best used for?
Amazon QuickSight is most often used for business intelligence, dashboard creation, data visualization. Of those, business intelligence and dashboard creation are not what Azure Machine Learning is typically brought in for.
What can Amazon QuickSight do that Azure Machine Learning cannot?
Amazon QuickSight covers SPICE In-memory Engine, ML Insights, Natural Language Queries, Embedded Analytics. Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry.

Answered from the vendors’ own pages

Amazon QuickSight: What does Amazon QuickSight cost per user?

QuickSight pricing varies by user role. Readers start at $3/month, Reader Pro at $20/month, Authors at $24/month, and Author Pro at $40/month. All prices are per user per month.

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.

Amazon QuickSight: Does QuickSight charge an infrastructure fee?

Yes, QuickSight charges an infrastructure fee of $250/month per account if the account has at least one Pro user, has Q&A enabled via topics, or has dashboard Q&A enabled.

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.

Amazon QuickSight: What are QuickSight's session capacity pricing options?

Session capacity pricing starts at $250/month for 500 sessions. Annual plans range from $20,000/year for 50,000 sessions to $258,000/year for 1,600,000 sessions, with volume discounts reducing unit costs from $0.50 to $0.16 per additional session.

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.

Amazon QuickSight: How much does additional SPICE storage cost?

Additional SPICE storage beyond the included 10 GB costs $0.38 per GB per month. Authors receive 10 GB included, but Readers do not.

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.

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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