Software · head to head
Domino Data Lab vs Kubeflow
The short version
- Each has a real cost: Domino Data Lab pricing is by quote only: the pricing page publishes no rate and no minimum, and the tier breakdown is behind a downloadable datasheet form; Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
- They diverge on capability: Domino Data Lab covers Reproducible environments, Kubeflow covers ML pipelines.
Where they differ
Only the attributes on which Domino Data Lab and Kubeflow actually diverge.
| Attribute | Domino Data Lab | Kubeflow |
|---|---|---|
| Pricing model | subscription | Unknown |
| Platforms | Web | Kubernetes |
| Founded | 2013 | 2017 |
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 Domino Data Lab
- Reproducible environments
- Model registry
- Model monitoring
- Collaboration
- Governance
- AWS
- Azure
- GCP
Only in Kubeflow
- ML pipelines
- Training operators
- Model serving
- Jupyter notebooks
- Hyperparameter tuning
- TensorFlow
- PyTorch
- XGBoost
Both cover
- Kubernetes
What people use each for
The jobs each tool is most often brought in to do.
Domino Data Lab
- Running reproducible data science workspaces and experiments on shared computenot Kubeflow
- Deploying and monitoring models with governance controlsnot Kubeflow
- Giving regulated enterprises a self managed MLOps platformnot Kubeflow
Kubeflow
- Machine learningnot Domino Data Lab
- Data analysisnot Domino Data Lab
- Model trainingnot Domino Data Lab
- Predictive analyticsnot Domino Data Lab
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Domino Data Lab
- Pricing is by quote only: the pricing page publishes no rate and no minimum, and the tier breakdown is behind a downloadable datasheet form
- Licensing is split by user type, with separate data science professional, data analyst, service account and admin licences
- FinOps, Nexus and Governance are paid add on modules rather than part of the platform
- Support level is a separate priced choice
- Self managed VPC or on premises deployment requires the Premium tier or higher
- No free trial is offered on the pricing page
Kubeflow
- Complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
- Resource-intensive infrastructure with minimal installs consuming significant CPU and memory
- Limited multi-tenancy support and multi-cloud setup leaves users largely on their own
- No native CI/CD integration, requiring custom glue code for versioning and automated deployments
- Debugging jobs and monitoring workloads often requires dropping down into raw Kubernetes commands
Pricing, plan by plan
Domino Data Lab
Free- TrialFree
- 14-day trial
- Full features
- EnterpriseFree
- Full platform
- Enterprise support
- SLA
Kubeflow
FreeNo published plan breakdown. See the Kubeflow review.
Which should you pick?
Choose Domino Data Lab if
- You need reproducible environments.
- You want to start without paying.
- You also want model registry.
Choose Kubeflow if
- You need ml pipelines.
- You want to start without paying.
- You work on Kubernetes.
- You also want training operators.
Questions people ask
- Is Domino Data Lab or Kubeflow better?
- Neither clearly leads. Domino Data Lab starts at Free and Kubeflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Domino Data Lab or Kubeflow?
- Domino Data Lab starts at Free and Kubeflow at Free.
- Does Domino Data Lab or Kubeflow run on more platforms?
- Domino Data Lab runs on Web. Kubeflow runs on Kubernetes.
- Can I use Domino Data Lab for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Domino Data Lab best used for?
- Domino Data Lab is most often used for running reproducible data science workspaces and experiments on shared compute, deploying and monitoring models with governance controls, giving regulated enterprises a self managed mlops platform. Of those, running reproducible data science workspaces and experiments on shared compute and deploying and monitoring models with governance controls are not what Kubeflow is typically brought in for.
- What can Domino Data Lab do that Kubeflow cannot?
- Domino Data Lab covers Reproducible environments, Model registry, Model monitoring, Collaboration. Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks. Both handle Kubernetes.
Answered from the vendors’ own pages
Kubeflow: Is Kubeflow free to use?
Yes, Kubeflow is free and open-source under Apache License 2.0. However, you pay for the underlying Kubernetes infrastructure, which typically costs $500 to $5,000 per month depending on scale and cloud provider.
SourceKubeflow: Do I need Kubernetes expertise to use Kubeflow?
Kubeflow requires significant Kubernetes and DevOps expertise. The installation deploys dozens of services and CRDs, often requiring manual configuration and troubleshooting. Data scientists typically need to convert scripts to containerized components.
SourceKubeflow: What platforms can Kubeflow run on?
Kubeflow runs on any Kubernetes-compliant cluster, including on-premise, AWS, Azure, Google Cloud, and hybrid environments. This multi-cloud portability is one of its key advantages over managed alternatives.
SourceKubeflow: How does Kubeflow compare to managed services like SageMaker?
Kubeflow offers multi-cloud portability and lower long-term costs but requires more operational overhead. SageMaker provides a fully managed experience with better UI and less infrastructure work, but creates vendor lock-in to AWS.
Source