Software · head to head
Seldon vs Hugging Face
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; Hugging Face model discovery across 3 million models lacks robust filtering and sorting by quality metrics
- They diverge on capability: Seldon covers Model serving, Hugging Face covers Model hub.
Where they differ
Only the attributes on which Seldon and Hugging Face actually diverge.
| Attribute | Seldon | Hugging Face |
|---|---|---|
| Pricing model | freemium | Unknown |
| Platforms | Linux | Web, API |
| Founded | 2014 | 2016 |
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 Hugging Face
- Model hub
- Datasets
- Spaces
- Transformers library
- GitHub
- Cloud providers
- MLOps tools
- Web support
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 Hugging Face
- Building multi-step inference pipelines with A/B tests and explainersnot Hugging Face
Hugging Face
- ai tools managementnot Seldon
- Workflow automationnot Seldon
- Reportingnot 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
Hugging Face
- Model discovery across 3 million models lacks robust filtering and sorting by quality metrics
- Community-driven content means variable model quality and documentation
- Private models and datasets require Pro subscription
- Enterprise support and SLAs require custom arrangements
Pricing, plan by plan
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Hugging Face
FreeNo published plan breakdown. See the Hugging Face 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 Hugging Face if
- You need model hub.
- You want to start without paying.
- You work on Web, API.
- You also want datasets.
Questions people ask
- Is Seldon or Hugging Face better?
- Neither clearly leads. Seldon starts at Free and Hugging Face at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Seldon or Hugging Face?
- Seldon starts at Free and Hugging Face at Free.
- Does Seldon or Hugging Face run on more platforms?
- Seldon runs on Linux. Hugging Face runs on Web, API.
- 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 Hugging Face is typically brought in for.
- What can Seldon do that Hugging Face cannot?
- Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. Hugging Face covers Model hub, Datasets, Spaces, Transformers library.
Answered from the vendors’ own pages
Hugging Face: Is Hugging Face free to use?
Yes. Hugging Face allows users to host and collaborate on unlimited public models, datasets, and applications at no cost. Models can be accessed and used freely from the Hub.
SourceHugging Face: How many models are available on Hugging Face?
Hugging Face Hub currently hosts nearly 3 million machine learning models across various tasks including text generation, image processing, and video generation.
SourceHugging Face: What is the Hugging Face Inference API?
Hugging Face provides access to 45,000+ models from leading AI providers through a single unified API with no service fees, simplifying access to diverse models.
SourceHugging Face: What content types does Hugging Face support?
Hugging Face supports text, image, video, audio, and 3D content models, allowing collaboration across multiple modalities and use cases.
SourceHugging Face: What is the transformers library?
Transformers is a Hugging Face library built for natural language processing applications, providing pre-built models and utilities for NLP tasks.
Source