Software · head to head
AWS SageMaker vs Seldon
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; Seldon production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
- They diverge on capability: AWS SageMaker covers Jupyter notebooks, Seldon covers Model serving.
Where they differ
Only the attributes on which AWS SageMaker and Seldon actually diverge.
| Attribute | AWS SageMaker | Seldon |
|---|---|---|
| Pricing model | Unknown | freemium |
| Platforms | Web | Linux |
| Founded | 2006 | 2014 |
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 Seldon
- Model serving
- A/B testing
- Canary deployments
- Outlier detection
- Model explainability
- Kubernetes
- Istio
- Prometheus
What people use each for
The jobs each tool is most often brought in to do.
AWS SageMaker
- Machine learningnot Seldon
- Data analysisnot Seldon
- Model trainingnot Seldon
- Predictive analyticsnot Seldon
Seldon
- Serving and routing machine learning models on Kubernetesnot AWS SageMaker
- Building multi-step inference pipelines with A/B tests and explainersnot 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
Seldon
- Production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
- The documented components carry both minimum and maximum supported versions, so newer Kubernetes and dependency versions are not automatically supported
- Dataflow Pipelines need an additional component that the docs recommend avoiding installing when pipelines are not used
- The Docker Compose install is offered as a lightweight alternative for environments without Kubernetes rather than as a production path
Pricing, plan by plan
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
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 Seldon if
- You need model serving.
- You want to start without paying.
- You work on Linux.
- You also want a/b testing.
Questions people ask
- Is AWS SageMaker or Seldon better?
- Neither clearly leads. AWS SageMaker starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, AWS SageMaker or Seldon?
- AWS SageMaker starts at Free and Seldon at Free.
- Does AWS SageMaker or Seldon run on more platforms?
- AWS SageMaker runs on Web. Seldon runs on Linux.
- 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 Seldon is typically brought in for.
- What can AWS SageMaker do that Seldon cannot?
- AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection.
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.
Source