Software · head to head
AWS SageMaker vs Ray
AWS SageMaker
Software
Build, train, and deploy machine learning models at scale
- From
- Free
- Rated
- -
The short version
- Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; Ray windows support is beta and multi node Ray clusters are untested on Windows
- They diverge on capability: AWS SageMaker covers Jupyter notebooks, Ray covers Distributed computing.
Where they differ
Only the attributes on which AWS SageMaker and Ray actually diverge.
| Attribute | AWS SageMaker | Ray |
|---|---|---|
| Pricing model | Unknown | freemium |
| Platforms | Web | Linux, Mac, Windows |
| Founded | 2006 | 2019 |
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 AWS SageMaker
- Jupyter notebooks
- Built-in algorithms
- Automatic model tuning
- One-click deployment
- Model monitoring
- S3
- Lambda
- Step Functions
Only in Ray
- Distributed computing
- Ray Train
- Ray Tune
- RLlib
- Ray Serve
- PyTorch
- TensorFlow
- Hugging Face
What people use each for
The jobs each tool is most often brought in to do.
AWS SageMaker
- Machine learningnot Ray
- Data analysisnot Ray
- Model trainingnot Ray
- Predictive analyticsnot Ray
Ray
- Distributing Python workloads across a clusternot AWS SageMaker
- Scaling model training and hyperparameter tuningnot AWS SageMaker
- Serving models and running distributed reinforcement learningnot AWS SageMaker
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
AWS SageMaker
- Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
- Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
- Does not include native job scheduling, requiring Lambda or EventBridge integration
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
Pricing, plan by plan
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
Ray
Free- Open SourceFree
- Full Ray framework
- All libraries
- Community support
- Anyscale PlatformFree
- Managed infrastructure
- Enterprise support
- SLAs
Which should you pick?
Choose AWS SageMaker if
- You need jupyter notebooks.
- You want to start without paying.
- You also want built-in algorithms.
Choose Ray if
- You need distributed computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want ray train.
Questions people ask
- Is AWS SageMaker or Ray better?
- Neither clearly leads. AWS SageMaker starts at Free and Ray at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, AWS SageMaker or Ray?
- AWS SageMaker starts at Free and Ray at Free.
- Does AWS SageMaker or Ray run on more platforms?
- AWS SageMaker runs on Web. Ray runs on Linux, Mac, Windows.
- Can I use AWS SageMaker for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is AWS SageMaker best used for?
- AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Ray is typically brought in for.
- What can AWS SageMaker do that Ray cannot?
- AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Ray covers Distributed computing, Ray Train, Ray Tune, RLlib.
Answered from the vendors’ own pages
AWS SageMaker: What is AWS SageMaker used for?
AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.
SourceAWS SageMaker: How is AWS SageMaker priced?
SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.
SourceAWS SageMaker: Does AWS SageMaker have a free tier?
Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.
SourceRelated pages
More on AWS SageMaker
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