Machine Learning · head to head
Azure Machine Learning vs BigQuery ML
Azure Machine Learning
Machine Learning
Microsoft's managed platform for training, tracking and deploying models on Azure
- From
- Free
- Rated
- -
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.; BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- They diverge on capability: Azure Machine Learning covers Workspace, BigQuery ML covers SQL-based ML.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Azure Machine Learning and BigQuery ML actually diverge.
| Attribute | Azure Machine Learning | BigQuery ML |
|---|---|---|
| Platforms | Azure Cloud | Web |
| Founded | 1975 | 2008 |
Identical on both: starting price (Free), pricing model (usage-based), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
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 BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- TensorFlow
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 BigQuery ML
- Training that needs to burst onto a GPU cluster occasionally without buying hardware, with the cluster scaling back to zero afterwardsnot BigQuery ML
- Regulated workloads that must stay inside a virtual network with private endpoints and auditable role-based accessnot BigQuery ML
- Teams already using MLflow who want the tracking interface they know backed by a managed service and enterprise identitynot BigQuery ML
BigQuery ML
- Training models in SQL without exporting datanot Azure Machine Learning
- Linear and logistic regression on warehouse datanot Azure Machine Learning
- K-means clustering and matrix factorisation for recommendationsnot Azure Machine Learning
- Time series forecasting with ARIMA_PLUSnot Azure Machine Learning
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot 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.
BigQuery ML
- Not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- Billed through BigQuery compute and storage rather than as its own product, so training cost tracks data scanned
- Remote models incur extra Agent Platform charges on top
- Externally trained model types such as boosted trees and AutoML run through Agent Platform rather than inside BigQuery
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
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
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.
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is Azure Machine Learning or BigQuery ML better?
- Neither clearly leads. Azure Machine Learning starts at Free and BigQuery ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Azure Machine Learning or BigQuery ML?
- Azure Machine Learning starts at Free and BigQuery ML at Free.
- Does Azure Machine Learning or BigQuery ML run on more platforms?
- Azure Machine Learning runs on Azure Cloud. BigQuery ML runs on Web.
- 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 BigQuery ML is typically brought in for.
- What can Azure Machine Learning do that BigQuery ML cannot?
- Azure Machine Learning covers Workspace, Compute clusters, MLflow-compatible tracking, Model registry. BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions.
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.
BigQuery ML: How much does Google Cloud BigQuery ML cost?
BigQuery ML pricing is not specified separately on Google Cloud's pricing page. It follows the same pay-as-you-go model as BigQuery, charging per terabyte of data scanned during analysis. Customers receive $300 in free credits and can use 20+ products free up to monthly limits.
SourceAzure 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.
BigQuery ML: Does Google Cloud offer a free trial?
Yes, new customers get $300 in free credits and all customers can use 20+ Google Cloud products free up to their monthly usage limits.
SourceAzure 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.
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.
Related pages
More on Azure Machine Learning
More on BigQuery ML
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