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Software · head to head

AWS SageMaker vs H2O.ai

AWS SageMaker logo

AWS SageMaker

Software

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite
H2O.ai logo

H2O.ai

Software

AI Cloud for building and deploying AI applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; 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: AWS SageMaker covers Jupyter notebooks, H2O.ai covers AutoML.

Where they differ

Only the attributes on which AWS SageMaker and H2O.ai actually diverge.

Attributes where AWS SageMaker and H2O.ai differ
AttributeAWS SageMakerH2O.ai
Pricing modelUnknownfreemium
PlatformsWebWeb, Cloud
Founded20062011

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 AWS SageMaker

  • Jupyter notebooks
  • Built-in algorithms
  • Automatic model tuning
  • One-click deployment
  • Model monitoring
  • S3
  • Lambda
  • Step Functions

Only in H2O.ai

  • AutoML
  • Distributed computing
  • Feature engineering
  • Model explainability
  • Time series forecasting
  • Spark
  • Hadoop
  • Python

Both cover

  • Web support

What people use each for

The jobs each tool is most often brought in to do.

AWS SageMaker

  • Machine learningnot H2O.ai
  • Data analysisnot H2O.ai
  • Model trainingnot H2O.ai
  • Predictive analyticsnot H2O.ai

H2O.ai

  • Distributed in-memory machine learning over large datasetsnot AWS SageMaker
  • Training and productionising models from R or Python against a shared H2O clusternot AWS SageMaker

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

AWS SageMaker

  • Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
  • Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
  • Does not include native job scheduling, requiring Lambda or EventBridge integration

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

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

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 AWS SageMaker if

  • You need jupyter notebooks.
  • You want to start without paying.
  • You also want built-in algorithms.
AWS SageMaker review

Choose H2O.ai if

  • You need automl.
  • You want to start without paying.
  • You work on Web, Cloud.
  • You also want distributed computing.
H2O.ai review

Questions people ask

Is AWS SageMaker or H2O.ai better?
Neither clearly leads. AWS SageMaker 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, AWS SageMaker or H2O.ai?
AWS SageMaker starts at Free and H2O.ai at Free.
Does AWS SageMaker or H2O.ai run on more platforms?
AWS SageMaker runs on Web. H2O.ai runs on Web, Cloud.
Can I use AWS SageMaker for free?
Both have a free tier, so you can try either at no cost before committing.
What is AWS SageMaker best used for?
AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what H2O.ai is typically brought in for.
What can AWS SageMaker do that H2O.ai cannot?
AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. H2O.ai covers AutoML, Distributed computing, Feature engineering, Model explainability. Both handle Web support.

Answered from the vendors’ own pages

AWS SageMaker: What is AWS SageMaker used for?

AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.

Source
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.

Source
AWS SageMaker: How is AWS SageMaker priced?

SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.

Source
H2O.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
AWS SageMaker: Does AWS SageMaker have a free tier?

Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.

Source
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