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Machine Learning · head to head

Azure Machine Learning vs Terra

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
Terra logo

Terra

Research

Open biomedical research platform for genomic analysis, priced only by the cloud you consume

From
Free
Rated
-
Full reviewWebsite

The short version

  • 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.; Terra the absence of a licence fee hides the real cost driver: an idle notebook virtual machine left running overnight, or a workspace bucket full of intermediate workflow outputs, quietly consumes grant money with no vendor invoice to prompt a review.
  • They diverge on capability: Azure Machine Learning covers Workspace, Terra covers No platform licence fee.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where Azure Machine Learning and Terra differ
AttributeAzure Machine LearningTerra
Pricing modelusage-basedOpen source, no licence fee
PlatformsAzure CloudWeb, Google Cloud, Microsoft Azure
CategoryMachine LearningResearch
Founded1975Unknown

Identical on both: starting price (Free), free tier (Yes), 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 Terra

  • No platform licence fee
  • Cloud pass-through billing
  • WDL workflow execution
  • Notebook environments
  • Controlled-access data
  • Workspace sharing
  • Compliance posture
  • Multi-cloud

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

Terra

  • A genomics lab running whole-genome alignment and variant calling pipelines without provisioning or maintaining a local clusternot Azure Machine Learning
  • A consortium sharing controlled-access human data across institutions where the data cannot be copied to each sitenot Azure Machine Learning
  • A health system analysing patient genomic data under a HIPAA business associate agreementnot Azure Machine Learning
  • A federal or federally funded project needing FedRAMP Moderate authorisation for its analysis environmentnot 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.

Terra

  • The absence of a licence fee hides the real cost driver: an idle notebook virtual machine left running overnight, or a workspace bucket full of intermediate workflow outputs, quietly consumes grant money with no vendor invoice to prompt a review.
  • Cost governance is entirely the customer responsibility, and grant-funded groups without a cloud engineer routinely overspend before anyone reads the Google or Azure bill in enough detail to find the cause.
  • It assumes genuine bioinformatics competence: writing or adapting WDL workflows, managing data tables and debugging failed tasks are all on you, so a wet lab without computational staff cannot use it productively.
  • Feature parity between the Google Cloud and Azure deployments is not identical, so a decision on cloud provider constrains which capabilities and datasets you can reach and is expensive to reverse later.
  • It is a research platform rather than a regulated clinical system: it is not a validated GxP environment and does not carry the qualification package a diagnostic or submission workflow would require, so clinical use needs additional infrastructure and validation you provide yourself.

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

Terra

Free
  • Terra platformFree
    • No charge for the platform, administration, security controls or frontline support
    • Workspaces, workflow execution and notebook environments included
    • Available on Google Cloud and Microsoft Azure
  • Cloud consumption$undefined/month
    • Google Cloud or Azure charges passed through at cost with no markup
    • Running notebook virtual machines around 0.22 USD per hour, roughly 0.027 USD per hour when paused
    • Storage billed continuously on workspace buckets and intermediate workflow outputs

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 Terra if

  • You need no platform licence fee.
  • You want to start without paying.
  • You work on Web, Google Cloud, Microsoft Azure.
  • You also want cloud pass-through billing.
Terra review

Questions people ask

Is Azure Machine Learning or Terra better?
Neither clearly leads. Azure Machine Learning starts at Free and Terra at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Azure Machine Learning or Terra?
Azure Machine Learning starts at Free and Terra at Free.
Does Azure Machine Learning or Terra run on more platforms?
Azure Machine Learning runs on Azure Cloud. Terra runs on Web, Google Cloud, Microsoft Azure.
Can I use Azure Machine Learning for free?
Both have a free tier, so you can try either at no cost before committing.
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 Terra is typically brought in for.
What can Azure Machine Learning do that Terra cannot?
Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. Terra covers No platform licence fee, Cloud pass-through billing, WDL workflow execution, Notebook environments.

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.

Terra: Is Terra really free?

The platform is. There is no licence fee. Everything you pay is Google Cloud or Azure compute, storage and egress, passed through at cost.

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.

Terra: What actually drives the bill?

Running virtual machines and stored data. Pausing notebooks drops a virtual machine from about 0.22 to about 0.027 dollars an hour, and deleting intermediate workflow outputs is the other main saving.

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.

Terra: Can I use it with protected health information?

Yes. Terra signs HIPAA business associate agreements and GDPR data processing agreements, and the Azure deployment holds FedRAMP Moderate authorisation.

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.

Terra: Is it validated for clinical or GxP use?

No. It is a research platform. Regulated clinical or submission workflows need validation and controls you build yourself.

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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