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Machine Learning · head to head

Azure Machine Learning vs DNAnexus

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
DNAnexus logo

DNAnexus

Research

Regulated cloud platform for genomic and multiomic data, with GxP and FedRAMP coverage

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.; DNAnexus no pricing is published, so an organisation cannot compare the total cost against running the same pipelines on its own cloud account until it is deep in a sales process.
  • They diverge on capability: Azure Machine Learning covers Workspace, DNAnexus covers Governed data storage.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Azure Machine Learning and DNAnexus differ
AttributeAzure Machine LearningDNAnexus
Starting priceFreeOn request
Pricing modelusage-basedquote
Free tierYesNo
PlatformsAzure CloudWeb, Linux
CategoryMachine LearningResearch
Founded1975Unknown

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 DNAnexus

  • Governed data storage
  • Workflow execution
  • App building
  • Controlled collaboration
  • Compliance posture
  • Cohort browsing

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

DNAnexus

  • A clinical diagnostics laboratory that needs a CLIA and CAP aligned environment for production sequencing pipelines without building the compliance evidence itselfnot Azure Machine Learning
  • A pharma sponsor analysing genomic endpoints in a registrational programme where 21 CFR Part 11 records are requirednot Azure Machine Learning
  • A biobank or consortium giving hundreds of external researchers governed access to a dataset too large to distributenot Azure Machine Learning
  • A diagnostics vendor packaging its pipeline as a versioned app so partner laboratories can run it without receiving the source codenot 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.

DNAnexus

  • No pricing is published, so an organisation cannot compare the total cost against running the same pipelines on its own cloud account until it is deep in a sales process.
  • Compute and storage are billed through the platform rather than at underlying cloud list price, so the compliance envelope carries a persistent margin on every terabyte and core hour, which grows with the science rather than staying fixed.
  • Data egress at genomic scale is slow and chargeable, so leaving the platform later is a real project rather than a contract decision, and that gravity weakens negotiating position at renewal.
  • The regulated and validated configurations sit in a higher commercial tier than the research configuration, so an organisation that starts in research mode and later needs GxP faces a repricing rather than a setting change.
  • Bioinformaticians used to running Nextflow on their own infrastructure find the platform abstractions constraining, and porting an existing pipeline into the app model is engineering work that does not carry over to any other platform.

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

DNAnexus

On request
  • DNAnexus Platform$undefined/year
    • Quoted per organisation through a sales process
    • Storage and compute billed through the platform rather than at cloud list price
    • Regulated and validated configurations are a separate commercial tier

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 DNAnexus if

  • You need governed data storage.
  • You work on Web, Linux.
  • You also want workflow execution.
DNAnexus review

Questions people ask

Is Azure Machine Learning or DNAnexus better?
Neither clearly leads. Azure Machine Learning starts at Free and DNAnexus 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 DNAnexus?
Azure Machine Learning has a free tier; the other does not. Paid plans start at Free for Azure Machine Learning and On request for DNAnexus.
Does Azure Machine Learning or DNAnexus run on more platforms?
Azure Machine Learning runs on Azure Cloud. DNAnexus runs on Web, Linux.
Can I use Azure Machine Learning for free?
Yes. Azure Machine Learning has a free tier, so you can try it without paying. DNAnexus 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 DNAnexus is typically brought in for.
What can Azure Machine Learning do that DNAnexus cannot?
Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. DNAnexus covers Governed data storage, Workflow execution, App building, Controlled 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.

DNAnexus: Why pay for DNAnexus instead of raw AWS?

For the compliance evidence. GxP, FedRAMP, CLIA, CAP and Part 11 coverage removes a validation programme most organisations would otherwise have to build and maintain themselves.

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.

DNAnexus: Is validated use included?

No. Regulated and validated configurations sit in a separate commercial tier from ordinary research use.

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.

DNAnexus: Can I get my data out?

Yes, but egress at genomic scale is chargeable and slow, so plan exit costs at the point of signing.

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.

DNAnexus: Does it host UK Biobank?

Yes, DNAnexus hosts the UK Biobank research analysis platform, which has its own access approval and cost terms.

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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