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Machine Learning · head to head

Azure Machine Learning vs DEAR Inventory

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
DEAR Inventory logo

DEAR Inventory

Inventory

Complete inventory and order management system

From
On request
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.; DEAR Inventory starting price of $349/month is high for small businesses
  • They diverge on capability: Azure Machine Learning covers Workspace, DEAR Inventory covers Inventory management.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Azure Machine Learning and DEAR Inventory differ
AttributeAzure Machine LearningDEAR Inventory
Starting priceFreeOn request
Pricing modelusage-basedsubscription
Free tierYesNo
PlatformsAzure CloudWeb, Mobile app, Cloud-based
CategoryMachine LearningInventory
Founded19752013

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 DEAR Inventory

  • Inventory management
  • Manufacturing
  • Purchase orders
  • Sales orders
  • Accounting
  • Xero
  • QuickBooks
  • Shopify

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

DEAR Inventory

  • Inventory management and trackingnot Azure Machine Learning
  • E-commerce integration and synchronizationnot Azure Machine Learning
  • Multi-channel order managementnot Azure Machine Learning
  • Stock and supply chain managementnot 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.

DEAR Inventory

  • Starting price of $349/month is high for small businesses
  • Base plan limited to 6 integrations; additional integrations require separate purchase
  • No published details on integration add-on costs
  • Redirects to Cin7.com, indicating product has been acquired or rebranded

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

DEAR Inventory

On request
  • Standard$249/month
    • Core features
    • 5 users
    • Standard support
  • Professional$449/month
    • Advanced manufacturing
    • 10 users
    • Priority support
  • Enterprise$849/month
    • Full features
    • Unlimited users
    • Dedicated 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 DEAR Inventory if

  • You need inventory management.
  • You work on Web, Mobile app, Cloud-based.
  • You also want manufacturing.
DEAR Inventory review

Questions people ask

Is Azure Machine Learning or DEAR Inventory better?
Neither clearly leads. Azure Machine Learning starts at Free and DEAR Inventory at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Azure Machine Learning or DEAR Inventory?
Azure Machine Learning has a free tier; the other does not. Paid plans start at Free for Azure Machine Learning and On request for DEAR Inventory.
Does Azure Machine Learning or DEAR Inventory run on more platforms?
Azure Machine Learning runs on Azure Cloud. DEAR Inventory runs on Web, Mobile app, Cloud-based.
Can I use Azure Machine Learning for free?
Yes. Azure Machine Learning has a free tier, so you can try it without paying. DEAR Inventory starts at On request.
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 DEAR Inventory is typically brought in for.
What can Azure Machine Learning do that DEAR Inventory cannot?
Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. DEAR Inventory covers Inventory management, Manufacturing, Purchase orders, Sales orders.

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.

DEAR Inventory: How much does DEAR Inventory cost?

DEAR Inventory (now Cin7) pricing starts at $349 per month for plans that accommodate up to 6 e-commerce and app integrations. Additional integrations are available for purchase.

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.

DEAR Inventory: Is there a free trial for DEAR Inventory?

Yes, Cin7 (formerly DEAR Inventory) offers a free trial option. Contact the vendor directly or visit their website to start a free trial.

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.

DEAR Inventory: What integrations does DEAR Inventory support?

DEAR Inventory base plans include up to 6 e-commerce and app integrations. Additional integrations beyond the base 6 are available but require separate purchase.

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.

DEAR Inventory: Is DEAR Inventory still available?

DEAR Inventory has been rebranded as Cin7. The Cin7 platform continues to provide inventory management with e-commerce integrations at $349/month starting price.

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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