Software · head to head
Seldon vs AWS SageMaker
AWS SageMaker
Software
Build, train, and deploy machine learning models at scale
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Seldon production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift; AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult
- They diverge on capability: Seldon covers Model serving, AWS SageMaker covers Jupyter notebooks.
Where they differ
Only the attributes on which Seldon and AWS SageMaker actually diverge.
| Attribute | Seldon | AWS SageMaker |
|---|---|---|
| Pricing model | freemium | Unknown |
| Platforms | Linux | Web |
| Founded | 2014 | 2006 |
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 Seldon
- Model serving
- A/B testing
- Canary deployments
- Outlier detection
- Model explainability
- Kubernetes
- Istio
- Prometheus
Only in AWS SageMaker
- Jupyter notebooks
- Built-in algorithms
- Automatic model tuning
- One-click deployment
- Model monitoring
- S3
- Lambda
- Step Functions
What people use each for
The jobs each tool is most often brought in to do.
Seldon
- Serving and routing machine learning models on Kubernetesnot AWS SageMaker
- Building multi-step inference pipelines with A/B tests and explainersnot AWS SageMaker
AWS SageMaker
- Machine learningnot Seldon
- Data analysisnot Seldon
- Model trainingnot Seldon
- Predictive analyticsnot Seldon
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Pricing, plan by plan
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
Which should you pick?
Choose Seldon if
- You need model serving.
- You want to start without paying.
- You work on Linux.
- You also want a/b testing.
Choose AWS SageMaker if
- You need jupyter notebooks.
- You want to start without paying.
- You also want built-in algorithms.
Questions people ask
- Is Seldon or AWS SageMaker better?
- Neither clearly leads. Seldon starts at Free and AWS SageMaker at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Seldon or AWS SageMaker?
- Seldon starts at Free and AWS SageMaker at Free.
- Does Seldon or AWS SageMaker run on more platforms?
- Seldon runs on Linux. AWS SageMaker runs on Web.
- Can I use Seldon for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Seldon best used for?
- Seldon is most often used for serving and routing machine learning models on kubernetes, building multi-step inference pipelines with a/b tests and explainers. Of those, serving and routing machine learning models on kubernetes and building multi-step inference pipelines with a/b tests and explainers are not what AWS SageMaker is typically brought in for.
- What can Seldon do that AWS SageMaker cannot?
- Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment.
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