Software · head to head
Seldon vs Google Vertex AI
Google Vertex AI
Software
Unified ML platform to build, deploy, and scale AI models
- 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; Google Vertex AI vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
- They diverge on capability: Seldon covers Model serving, Google Vertex AI covers AutoML.
Where they differ
Only the attributes on which Seldon and Google Vertex AI actually diverge.
| Attribute | Seldon | Google Vertex AI |
|---|---|---|
| Starting price | Free | On request |
| Pricing model | freemium | Unknown |
| Free tier | Yes | No |
| Platforms | Linux | Cloud, Web |
| Founded | 2014 | 2008 |
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 Google Vertex AI
- AutoML
- Custom training
- Feature Store
- Model monitoring
- Prediction serving
- BigQuery
- Cloud Storage
- TensorFlow
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 Google Vertex AI
- Building multi-step inference pipelines with A/B tests and explainersnot Google Vertex AI
Google Vertex AI
- 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
Google Vertex AI
- Vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
- Requires familiarity with Google Cloud Platform infrastructure and concepts
- Cost can escalate quickly with large training and inference workloads
Pricing, plan by plan
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Google Vertex AI
On requestNo published plan breakdown. See the Google Vertex AI 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 Google Vertex AI if
- You need automl.
- You work on Cloud, Web.
- You also want custom training.
Questions people ask
- Is Seldon or Google Vertex AI better?
- Neither clearly leads. Seldon starts at Free and Google Vertex AI at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Seldon or Google Vertex AI?
- Seldon has a free tier; the other does not. Paid plans start at Free for Seldon and On request for Google Vertex AI.
- Does Seldon or Google Vertex AI run on more platforms?
- Seldon runs on Linux. Google Vertex AI runs on Cloud, Web.
- Can I use Seldon for free?
- Yes. Seldon has a free tier, so you can try it without paying. Google Vertex AI 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 Google Vertex AI is typically brought in for.
- What can Seldon do that Google Vertex AI cannot?
- Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. Google Vertex AI covers AutoML, Custom training, Feature Store, Model monitoring.
Answered from the vendors’ own pages
Google Vertex AI: What is the pricing model for Google Vertex AI?
Vertex AI uses a pay-as-you-go model with no upfront costs or lock-in fees. Costs vary by service: training is billed by compute resources and time (30-second increments), online predictions by machine type per hour, and batch predictions by compute time or per-record for specific AutoML types.
SourceGoogle Vertex AI: What types of data can Vertex AI handle?
Vertex AI supports image, video, text, and tabular data types with tools for uploading, storing, and managing large datasets.
SourceGoogle Vertex AI: Does Vertex AI support custom model training?
Yes. Vertex AI supports both AutoML for automated machine learning and custom training code in Python, R, and other languages.
SourceGoogle Vertex AI: What deployment options are available in Vertex AI?
Vertex AI supports online predictions for real-time use cases and batch predictions for large-scale processing.
Source