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Databases · head to head

Amazon Redshift vs Azure Machine Learning

Amazon Redshift logo

Amazon Redshift

Databases

Fast, scalable cloud data warehouse from AWS

From
Free
Rated
-
Full reviewWebsite
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

The short version

  • Each has a real cost: Amazon Redshift on-demand pricing runs up to 75% higher than competitors like Snowflake and BigQuery; 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.
  • They diverge on capability: Amazon Redshift covers Columnar Storage, Azure Machine Learning covers Workspace.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Amazon Redshift and Azure Machine Learning differ
AttributeAmazon RedshiftAzure Machine Learning
PlatformsWebAzure Cloud
CategoryDatabasesMachine Learning
Founded20121975

Identical on both: starting price (Free), pricing model (usage-based), 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 Amazon Redshift

  • Columnar Storage
  • Massively Parallel
  • Machine Learning
  • AQUA Acceleration
  • Data Sharing
  • Federated Query
  • Concurrency Scaling
  • S3

Only in Azure Machine Learning

  • Workspace
  • Compute clusters
  • MLflow-compatible tracking
  • Model registry
  • Managed online endpoints
  • Batch endpoints
  • Automated machine learning
  • Pipelines

What people use each for

The jobs each tool is most often brought in to do.

Amazon Redshift

  • Business intelligencenot Azure Machine Learning
  • Data warehousingnot Azure Machine Learning
  • Real-time analyticsnot Azure Machine Learning
  • Reportingnot Azure Machine Learning
  • Machine learningnot Azure Machine Learning

Azure Machine Learning

  • Enterprises standardised on Azure where using a different cloud for machine learning would mean a fresh security and compliance reviewnot Amazon Redshift
  • Training that needs to burst onto a GPU cluster occasionally without buying hardware, with the cluster scaling back to zero afterwardsnot Amazon Redshift
  • Regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based accessnot Amazon Redshift
  • Teams already using MLflow who want the tracking interface they know backed by a managed service and enterprise identitynot Amazon Redshift

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Amazon Redshift

  • On-demand pricing runs up to 75% higher than competitors like Snowflake and BigQuery
  • Requires significant manual tuning including managing concurrency scaling costs and configuring Workload Management queues
  • Performance degrades without proper design of distribution keys and sort keys
  • Limited elastic resize options - can only halve or double current cluster size
  • AWS lock-in makes it unsuitable for multi-cloud architectures

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.

Pricing, plan by plan

Amazon Redshift

Free
  • Free TrialFree
    • 750 DC2.Large hours
    • 2 months free
    • Full features
  • On-Demand$0.25/hour
    • Pay per node hour
    • All features
    • Standard support

Azure Machine Learning

Free
  • Free TierFree
    • Limited compute
    • Basic features
  • Pay-as-you-go$0.05/hour
    • Full platform
    • All compute options
    • Enterprise features

Which should you pick?

Choose Amazon Redshift if

  • You need columnar storage.
  • You want to start without paying.
  • You also want massively parallel.
Amazon Redshift review

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

Questions people ask

Is Amazon Redshift or Azure Machine Learning better?
Neither clearly leads. Amazon Redshift starts at Free and Azure Machine Learning at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Amazon Redshift or Azure Machine Learning?
Amazon Redshift starts at Free and Azure Machine Learning at Free.
Does Amazon Redshift or Azure Machine Learning run on more platforms?
Amazon Redshift runs on Web. Azure Machine Learning runs on Azure Cloud.
Can I use Amazon Redshift for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amazon Redshift best used for?
Amazon Redshift is most often used for business intelligence, data warehousing, real-time analytics, reporting. Of those, business intelligence and data warehousing are not what Azure Machine Learning is typically brought in for.
What can Amazon Redshift do that Azure Machine Learning cannot?
Amazon Redshift covers Columnar Storage, Massively Parallel, Machine Learning, AQUA Acceleration. Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry.

Answered from the vendors’ own pages

Amazon Redshift: What deployment options does Amazon Redshift offer?

Redshift offers Provisioned Cluster (with RA3 or DC2 nodes) and Serverless options to match varying workloads. The new Redshift RG instance family, powered by Graviton, delivers 2.4x faster performance than RA3 at 30% lower cost per vCPU.

Source
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.

Amazon Redshift: What does Amazon Redshift cost?

Provisioned cluster pricing: RA3 on-demand starts at $1.086/hour for ra3.xlplus. Serverless costs approximately $0.375 per RPU-hour with 4-RPU minimum (roughly $1.50/hour active workload). Managed storage costs $0.024/GB-month.

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.

Amazon Redshift: Does Redshift work with data lakes?

Yes, Redshift's integrated data lake query engine processes workloads on Apache Iceberg tables and other supported formats in Amazon S3, allowing you to run SQL analytics across your data warehouse and data lake from the same engine.

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.

Amazon Redshift: Is there a free tier for Amazon Redshift?

AWS offers a free trial with $300 USD in Serverless credits valid for 90 days, but Redshift is not part of the permanent AWS Free Tier.

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.

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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