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Machine Learning · head to head

Azure Machine Learning vs Rapid7 InsightVM

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
Rapid7 InsightVM logo

Rapid7 InsightVM

Cybersecurity

Vulnerability management at scale

From
$1.62/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.; Rapid7 InsightVM insightVM is now sold inside Exposure Command rather than as a standalone product
  • They diverge on capability: Azure Machine Learning covers Workspace, Rapid7 InsightVM covers Asset discovery.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Azure Machine Learning and Rapid7 InsightVM actually diverge.

Attributes where Azure Machine Learning and Rapid7 InsightVM differ
AttributeAzure Machine LearningRapid7 InsightVM
Starting priceFree$1.62/month
Free tierYesNo
PlatformsAzure CloudWeb, Cloud, Api
CategoryMachine LearningCybersecurity
Founded19752000

Identical on both: pricing model (usage-based), 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 Rapid7 InsightVM

  • Asset discovery
  • Vulnerability scanning
  • Risk prioritization
  • Threat intelligence
  • Custom policies
  • Remediation tracking
  • Compliance reporting
  • API access

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 Rapid7 InsightVM
  • Training that needs to burst onto a GPU cluster occasionally without buying hardware, with the cluster scaling back to zero afterwardsnot Rapid7 InsightVM
  • Regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based accessnot Rapid7 InsightVM
  • Teams already using MLflow who want the tracking interface they know backed by a managed service and enterprise identitynot Rapid7 InsightVM

Rapid7 InsightVM

  • Vulnerability management and risk assessmentnot Azure Machine Learning
  • Web application security scanningnot Azure Machine Learning
  • Cloud infrastructure security monitoringnot 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.

Rapid7 InsightVM

  • InsightVM is now sold inside Exposure Command rather than as a standalone product
  • Rapid7 states pricing is not publicly listed and is provided by custom quote based on the customer's environment
  • Billing counts billable assets including devices, software, identities, cloud compute instances and applications, so the asset count grows faster than a device inventory suggests
  • Cloud security, compliance, infrastructure as code scanning and application security testing require the Ultimate package rather than Essentials
  • Volume discounts are described as commonly available for larger asset counts, so smaller buyers pay the higher unit rate

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

Rapid7 InsightVM

$1.62/month
  • InsightVM$1.62/month per asset
    • Vulnerability risk management
    • Unlimited user accounts
    • 24/7 technical support
  • InsightAppSec$175/month
    • Web application security
    • Per application pricing
    • Unlimited user accounts
  • InsightCloudsec$5775/month
    • Cloud security
    • Up to 500 instances
    • Unlimited user accounts

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 Rapid7 InsightVM if

  • You need asset discovery.
  • You work on Web, Cloud, Api.
  • You also want vulnerability scanning.
Rapid7 InsightVM review

Questions people ask

Is Azure Machine Learning or Rapid7 InsightVM better?
Neither clearly leads. Azure Machine Learning starts at Free and Rapid7 InsightVM at $1.62/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Azure Machine Learning or Rapid7 InsightVM?
Azure Machine Learning has a free tier; the other does not. Paid plans start at Free for Azure Machine Learning and $1.62/month for Rapid7 InsightVM.
Does Azure Machine Learning or Rapid7 InsightVM run on more platforms?
Azure Machine Learning runs on Azure Cloud. Rapid7 InsightVM 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. Rapid7 InsightVM starts at $1.62/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 Rapid7 InsightVM is typically brought in for.
What can Azure Machine Learning do that Rapid7 InsightVM cannot?
Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. Rapid7 InsightVM covers Asset discovery, Vulnerability scanning, Risk prioritization, Threat intelligence.

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.

Rapid7 InsightVM: What is the starting price for Rapid7 InsightVM?

InsightVM starts at $1.62 per month per asset, with example pricing provided for 500-asset deployments.

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.

Rapid7 InsightVM: How much does InsightAppSec cost?

InsightAppSec starts at $175 per month per application for web application security scanning.

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.

Rapid7 InsightVM: What does InsightCloudsec cover?

InsightCloudsec starts at $5,775 per month for cloud security monitoring of up to 500 instances.

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.

Rapid7 InsightVM: Do Rapid7 products include unlimited user accounts?

Yes, all Rapid7 products include unlimited user accounts, 24/7 technical support, single sign-on, and customer-success team access.

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.

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