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Machine Learning · head to head

Azure Machine Learning vs Qualys VMDR

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
Qualys VMDR logo

Qualys VMDR

Cybersecurity

Cloud-delivered vulnerability management licensed per asset, using scanner appliances and a lightweight agent.

From
$3/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.; Qualys VMDR licensing is per asset and each capability is its own subscription, so patch management, endpoint detection, web application scanning, container security and policy compliance are separate line items, and the platform demonstrated in a proof of concept is rarely the platform in the quote.
  • They diverge on capability: Azure Machine Learning covers Workspace, Qualys VMDR covers Cloud Agent.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Azure Machine Learning and Qualys VMDR differ
AttributeAzure Machine LearningQualys VMDR
Starting priceFree$3/month
Pricing modelusage-basedsubscription
Free tierYesNo
PlatformsAzure CloudWeb, Cloud, Api
CategoryMachine LearningCybersecurity
Founded19751999

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 Qualys VMDR

  • Cloud Agent
  • Scanner appliances
  • Authenticated scanning
  • TruRisk scoring
  • Asset inventory
  • Patch Management
  • Cloud connectors
  • Container sensor

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

Qualys VMDR

  • A hybrid workforce where scheduled network scans miss most laptops and continuous agent-based assessment is the only way to get real coveragenot Azure Machine Learning
  • PCI DSS external scanning where an approved scanning vendor report is a contractual requirementnot Azure Machine Learning
  • An estate spanning datacentre, multiple public clouds and endpoints that needs one vulnerability view rather than three toolsnot Azure Machine Learning
  • Organisations replacing a manual patch-verification process with agent-reported evidence that a fix actually landednot 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.

Qualys VMDR

  • Licensing is per asset and each capability is its own subscription, so patch management, endpoint detection, web application scanning, container security and policy compliance are separate line items, and the platform demonstrated in a proof of concept is rarely the platform in the quote.
  • Ephemeral cloud instances consume asset entitlement until they age out of inventory, so an autoscaling group that creates and destroys hosts hourly can burn licence capacity on machines that existed for minutes, and controlling that means tuning purge policies rather than tuning the cloud.
  • Authenticated scanning produces materially better results than unauthenticated, but it requires storing and rotating privileged credentials for every target platform, which is a security project in its own right, and teams that skip it receive reports full of unconfirmed potential findings that nobody trusts.
  • The console is a set of modules with separate interfaces, search syntaxes and report engines, so an analyst moving between vulnerability management, policy compliance and web application scanning learns each one, and cross-module reporting usually ends in a spreadsheet or a script against the API.
  • The tool surfaces findings far faster than any organisation can remediate them, and it does not solve the ownership problem: without an agreed prioritisation policy and a named owner in IT operations, a first scan producing tens of thousands of findings becomes a dashboard that everyone learns to ignore.

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

Qualys VMDR

$3/month
  • VMDR$3/month
    • Per asset
    • Vulnerability scanning
    • Detection
  • VMDR+$5/month
    • All VMDR features
    • Advanced analytics
    • Cloud integration
  • VMDR Complete$7/month
    • All VMDR+ features
    • Threat intel
    • Response automation

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 Qualys VMDR if

  • You need cloud agent.
  • You work on Web, Cloud, Api.
  • You also want scanner appliances.
Qualys VMDR review

Questions people ask

Is Azure Machine Learning or Qualys VMDR better?
Neither clearly leads. Azure Machine Learning starts at Free and Qualys VMDR at $3/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Azure Machine Learning or Qualys VMDR?
Azure Machine Learning has a free tier; the other does not. Paid plans start at Free for Azure Machine Learning and $3/month for Qualys VMDR.
Does Azure Machine Learning or Qualys VMDR run on more platforms?
Azure Machine Learning runs on Azure Cloud. Qualys VMDR runs on Web, Cloud, Api.
Can I use Azure Machine Learning for free?
Yes. Azure Machine Learning has a free tier, so you can try it without paying. Qualys VMDR starts at $3/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 Qualys VMDR is typically brought in for.
What can Azure Machine Learning do that Qualys VMDR cannot?
Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. Qualys VMDR covers Cloud Agent, Scanner appliances, Authenticated scanning, TruRisk scoring.

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.

Qualys VMDR: Agent or scanner: which do I need?

Usually both. The agent covers endpoints and servers you control and gives continuous data; scanners cover devices you cannot install an agent on, such as network gear, printers and appliances, and provide the external perimeter view.

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.

Qualys VMDR: Does it patch as well as detect?

Yes, through the Patch Management module, which is a separate subscription using the same agent. Core VMDR detects and prioritises but does not deploy fixes.

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.

Qualys VMDR: How are assets counted for licensing?

By the number of assets in inventory, which includes cloud instances and containers depending on the modules in use. Short-lived cloud assets count until they are purged, so purge settings directly affect what you consume.

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.

Qualys VMDR: Can I keep the data in a specific region?

Yes. Qualys operates several regional platform instances and you choose which one your subscription lives on. Moving between them later is not trivial, so decide before onboarding.

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.

Qualys VMDR: Does it scan web applications?

Web Application Scanning is a separate module licensed per application, not part of core VMDR, and it is a dynamic scanner with the usual limits around authenticated flows in single-page applications.

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