Software · head to head
Ray vs Azure Machine Learning
Azure Machine Learning
Software
Enterprise-grade machine learning service
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Ray windows support is beta and multi node Ray clusters are untested on Windows; Azure Machine Learning requires knowledge of Azure ecosystem and integration with other Azure services
- They diverge on capability: Ray covers Distributed computing, Azure Machine Learning covers Automated ML.
Where they differ
Only the attributes on which Ray and Azure Machine Learning actually diverge.
| Attribute | Ray | Azure Machine Learning |
|---|---|---|
| Pricing model | freemium | usage-based |
| Platforms | Linux, Mac, Windows | Azure Cloud |
| Founded | 2019 | 1975 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).
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 Ray
- Distributed computing
- Ray Train
- Ray Tune
- RLlib
- Ray Serve
- PyTorch
- TensorFlow
- Hugging Face
Only in Azure Machine Learning
- Automated ML
- Designer (drag-and-drop)
- Notebooks
- MLOps
- Model registry
- Azure Blob Storage
- Azure DevOps
- Power BI
What people use each for
The jobs each tool is most often brought in to do.
Ray
- Distributing Python workloads across a clusternot Azure Machine Learning
- Scaling model training and hyperparameter tuningnot Azure Machine Learning
- Serving models and running distributed reinforcement learningnot Azure Machine Learning
Azure Machine Learning
- Machine learningnot Ray
- Data analysisnot Ray
- Model trainingnot Ray
- Predictive analyticsnot Ray
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Ray
- Windows support is beta and multi node Ray clusters are untested on Windows
- Windows lacks copy on write forking, which raises memory requirements, and Ray code assumes UNIX filenames
- Multi node clusters are untested on Apple Silicon Macs
- The Java API is experimental and community supported only, and requires matching Java and Python versions
- Python 3.13 support is beta
Azure Machine Learning
- Requires knowledge of Azure ecosystem and integration with other Azure services
- Compute resources for training and inference generate separate charges
Pricing, plan by plan
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
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 Ray if
- You need distributed computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want ray train.
Choose Azure Machine Learning if
- You need automated ml.
- You want to start without paying.
- You work on Azure Cloud.
- You also want designer (drag-and-drop).
Questions people ask
- Is Ray or Azure Machine Learning better?
- Neither clearly leads. Ray 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, Ray or Azure Machine Learning?
- Ray starts at Free and Azure Machine Learning at Free.
- Does Ray or Azure Machine Learning run on more platforms?
- Ray runs on Linux, Mac, Windows. Azure Machine Learning runs on Azure Cloud.
- Can I use Ray for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Ray best used for?
- Ray is most often used for distributing python workloads across a cluster, scaling model training and hyperparameter tuning, serving models and running distributed reinforcement learning. Of those, distributing python workloads across a cluster and scaling model training and hyperparameter tuning are not what Azure Machine Learning is typically brought in for.
- What can Ray do that Azure Machine Learning cannot?
- Ray covers Distributed computing, Ray Train, Ray Tune, RLlib. Azure Machine Learning covers Automated ML, Designer (drag-and-drop), Notebooks, MLOps.
Answered from the vendors’ own pages
Azure Machine Learning: Does Azure Machine Learning have any platform licensing fees?
No, Azure Machine Learning carries no extra cost. You only pay for the underlying compute resources utilized during model training or inference.
SourceAzure Machine Learning: What AutoML capabilities does Azure Machine Learning provide?
Azure Machine Learning supports automated model creation for classification, regression, vision, and natural language processing tasks.
SourceAzure Machine Learning: Does Azure ML support language model fine-tuning?
Yes, Azure Machine Learning supports fine-tuning of foundation models from providers including OpenAI, Meta, Hugging Face, and Cohere.
SourceAzure Machine Learning: What MLOps features are included?
Azure ML includes end-to-end pipeline automation with CI/CD capabilities, managed endpoints for model deployment, and monitoring tools.
SourceAzure Machine Learning: Can I access foundation models from multiple vendors?
Yes, Azure Machine Learning provides access to a model catalog with foundation models from Microsoft, OpenAI, Hugging Face, Meta, and Cohere.
Source