Software · head to head
DVC vs Seldon
The short version
- Each has a real cost: DVC dVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.; 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: DVC covers Data versioning, Seldon covers Model serving.
Where they differ
Only the attributes on which DVC and Seldon actually diverge.
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 DVC
- Data versioning
- Pipeline management
- Experiment tracking
- Remote storage
- Git integration
- Git
- S3
- Azure Blob
Only in Seldon
- Model serving
- A/B testing
- Canary deployments
- Outlier detection
- Model explainability
- Kubernetes
- Istio
- Prometheus
Both cover
- Linux support
What people use each for
The jobs each tool is most often brought in to do.
DVC
- Machine learningnot Seldon
- Data analysisnot Seldon
- Model trainingnot Seldon
- Predictive analyticsnot Seldon
Seldon
- Serving and routing machine learning models on Kubernetesnot DVC
- Building multi-step inference pipelines with A/B tests and explainersnot DVC
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
DVC
- DVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.
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
DVC
Free- Open SourceFree
- Data versioning
- Pipeline management
- Experiment tracking
- DVC StudioFree
- Web UI
- Team collaboration
- Visualizations
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Which should you pick?
Choose DVC if
- You need data versioning.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want pipeline management.
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 DVC or Seldon better?
- Neither clearly leads. DVC 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, DVC or Seldon?
- DVC starts at Free and Seldon at Free.
- Does DVC or Seldon run on more platforms?
- DVC runs on Linux, Mac, Windows. Seldon runs on Linux.
- Can I use DVC for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is DVC best used for?
- DVC 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 DVC do that Seldon cannot?
- DVC covers Data versioning, Pipeline management, Experiment tracking, Remote storage. Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. Both handle Linux support.