Software · head to head
Seldon vs H2O.ai
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; H2O.ai java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported
- They diverge on capability: Seldon covers Model serving, H2O.ai covers AutoML.
Where they differ
Only the attributes on which Seldon and H2O.ai actually diverge.
Identical on both: starting price (Free), pricing model (freemium), 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
- Kubernetes
- Istio
- Prometheus
- Grafana
Only in H2O.ai
- AutoML
- Distributed computing
- Feature engineering
- Time series forecasting
- Spark
- Hadoop
- Python
- R
Both cover
- Model explainability
- Linux 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 H2O.ai
- Building multi-step inference pipelines with A/B tests and explainersnot H2O.ai
H2O.ai
- Distributed in-memory machine learning over large datasetsnot Seldon
- Training and productionising models from R or Python against a shared H2O clusternot 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
H2O.ai
- Java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported
- Supported Java versions stop at Java SE 17; newer versions only run by forcing an unsupported version flag and are guaranteed for experiments rather than production
- H2O-3 only supports numpy below version 2, so a numpy 2 environment must be downgraded
- Supported Python versions are limited to 3.7 through 3.11
- The Flow web UI requires an internet browser and is the only graphical interface
Pricing, plan by plan
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
H2O.ai
Free- H2O-3 Open SourceFree
- Core algorithms
- AutoML
- Community support
- Driverless AIFree
- Automatic feature engineering
- Model explainability
- Enterprise support
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 H2O.ai if
- You need automl.
- You want to start without paying.
- You work on Web, Cloud.
- You also want distributed computing.
Questions people ask
- Is Seldon or H2O.ai better?
- Neither clearly leads. Seldon starts at Free and H2O.ai at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Seldon or H2O.ai?
- Seldon starts at Free and H2O.ai at Free.
- Does Seldon or H2O.ai run on more platforms?
- Seldon runs on Linux. H2O.ai runs on Web, Cloud.
- 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 H2O.ai is typically brought in for.
- What can Seldon do that H2O.ai cannot?
- Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. H2O.ai covers AutoML, Distributed computing, Feature engineering, Time series forecasting. Both handle Model explainability, Linux support.
Answered from the vendors’ own pages
H2O.ai: Is H2O open source and free?
Yes. H2O-3 OSS is free and Apache-licensed, designed for self-managed and experimental workflows. H2O.ai also offers enterprise cloud solutions with additional features.
SourceH2O.ai: How many companies use H2O's open source platform?
Over 18,000 companies across Finance, Insurance, Healthcare, Retail, Telco, Sales, and Marketing use H2O's open-source machine learning platform.
Source