Software · head to head
Seldon vs DataRobot
DataRobot
Software
Enterprise AI platform for automated machine learning
- From
- On request
- Rated
- -
The short version
- Only Seldon has a free tier, so it costs nothing to try first.
- 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; DataRobot model transparency is limited, often resembling a black box with limited explainability
- They diverge on capability: Seldon covers Model serving, DataRobot covers Automated ML.
Where they differ
Only the attributes on which Seldon and DataRobot actually diverge.
Identical on both: 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 DataRobot
- Automated ML
- Model deployment
- Time series
- MLOps
- Model monitoring
- Snowflake
- Databricks
- AWS
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 DataRobot
- Building multi-step inference pipelines with A/B tests and explainersnot DataRobot
DataRobot
- 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
DataRobot
- Model transparency is limited, often resembling a black box with limited explainability
- Requires integration with separate data manipulation tools for complex data transformation
- Lacks native Python and R code customization for proprietary algorithms
- Dependence on cloud connectivity means offline capabilities are not available
- Uploading sensitive data to third-party servers raises data privacy and security concerns
Pricing, plan by plan
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
DataRobot
On request- TrialFree
- Limited access
- Basic features
- EnterpriseFree
- Full platform
- AutoML
- MLOps
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.
Questions people ask
- Is Seldon or DataRobot better?
- Neither clearly leads. Seldon starts at Free and DataRobot at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Seldon or DataRobot?
- Seldon has a free tier; the other does not. Paid plans start at Free for Seldon and On request for DataRobot.
- Does Seldon or DataRobot run on more platforms?
- Seldon runs on Linux. DataRobot runs on Web.
- Can I use Seldon for free?
- Yes. Seldon has a free tier, so you can try it without paying. DataRobot starts at On request.
- 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 DataRobot is typically brought in for.
- What can Seldon do that DataRobot cannot?
- Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. DataRobot covers Automated ML, Model deployment, Time series, MLOps.
Answered from the vendors’ own pages
DataRobot: Does DataRobot require data science expertise?
DataRobot automates much of the ML pipeline including data preparation, feature engineering, and model selection, making it more accessible to non-experts, though it is still an enterprise platform.
SourceDataRobot: What does DataRobot cost?
DataRobot uses custom enterprise pricing with typical starting costs around $2,500 per month for smaller organizations. For 10 users, monthly costs range from $15,000 to $20,000. Implementation and professional services are 20-40% of first-year contract value.
SourceDataRobot: Does DataRobot support generative AI?
Yes, DataRobot offers generative AI capabilities with API-first integrations for LLMs, vector databases, and embedding models.
SourceDataRobot: Can DataRobot handle unstructured data?
Yes, DataRobot supports machine learning on both structured and unstructured data, including deep learning, NLP, and image analysis.
Source