Amazon SageMaker vs H2O.ai comparison

Amazon Web Services (AWS) and H2O.ai are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.0, while H2O.ai is ranked #20 with an average rating of 7.0. Amazon Web Services (AWS) holds a 6.2% mindshare in Data Science Platforms, compared to H2O.ai’s 1.8% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of H2O.ai users who would recommend it.

Amazon SageMaker

Read 37 Amazon SageMaker reviews

7,896 Views
4,738 Comparison Views

91% willing to recommend

H2O.ai

Read 8 H2O.ai reviews

1,198 Views
1,138 Comparison Views

100% willing to recommend

Amazon SageMaker

H2O.ai

Comparison Buyer's Guide

Download the report

Executive SummaryUpdated on Dec 5, 2024

H2O.ai and Amazon SageMaker are competing in the machine learning platform category. H2O.ai has an upper hand in feature richness and ease of deployment, while Amazon SageMaker stands out for its scalability and robust customer service.

Features: H2O.ai offers AutoML, time series forecasting, and AI-driven solutions. It has a collaborative environment and advanced algorithms suited for data scientists. Amazon SageMaker integrates tightly with AWS and supports various frameworks, providing SageMaker Studio for comprehensive development.

Room for Improvement: H2O.ai could enhance its documentation, expand integration capabilities with more cloud services, and improve user interface design. Amazon SageMaker might benefit from simplifying its pricing structure, offering more flexibility in resource selection, and improving integration with non-AWS services.

Ease of Deployment and Customer Service: H2O.ai is noted for its intuitive deployment process and offers prompt support through direct and community channels. Amazon SageMaker ensures efficient deployment with AWS integration, offering vast support options and detailed documentation.

Pricing and ROI: H2O.ai generally has lower setup costs and leverages open-source components. Amazon SageMaker, despite potentially higher initial prices, offers a better ROI with seamless AWS integration, making it cost-effective for large enterprises.

To learn more, read our detailed Amazon SageMaker vs. H2O.ai Report (Updated: June 2025).

Buyer's Guide

Amazon SageMaker vs. H2O.ai

June 2025

Download the complete report

Helped 861,390 peers since 2012

Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

Amazon SageMaker

Ranking in Data Science Platforms

3rd

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

AI Development Platforms (5th)

H2O.ai

Ranking in Data Science Platforms

20th

Average Rating

7.6

Reviews Sentiment

7.2

Number of Reviews

Ranking in other categories

Model Monitoring (5th)

Mindshare comparison

As of July 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.2%, down from 9.3% compared to the previous year. The mindshare of H2O.ai is 1.8%, up from 1.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

Create innovative assistants with seamless data integration for large-scale projects

The various integration options available in Amazon SageMaker ( /products/amazon-sagemaker-reviews ), such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue ( /products/aws-glue-reviews ) integrate well for data transformations. The Databricks ( /products/databricks-reviews ) integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow ( /products/tensorflow-reviews ), PyTorch ( /products/pytorch-reviews ), and MXNet ( /products/mxnet-reviews ), and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

Read full review

Kashif Yaseen

Trainee Decision Scientist at a tech services company with 1,001-5,000 employees

Plug-and-play convenience enhances productivity but needs better multimodal support

We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI The solution was plug-and-play, meaning most of the components were handled by the solution itself rather than building them from scratch. This was useful for our banking…

Read full review

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."

"I have seen a return on investment, probably a factor of four or five."

"They are doing a good job of evolving."

"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."

"It's user-friendly for business teams as they can understand many aspects through the AWS interface."

"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."

"The product aggregates everything we need to build and deploy machine learning models in one place."

"The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use."

More Amazon SageMaker pros

"The most valuable feature of H2O.ai is that it is plug-and-play."

"One of the most interesting features of the product is their driverless component. The driverless component allows you to test several different algorithms along with navigating you through choosing the best algorithm."

"The ease of use in connecting to our cluster machines."

"AutoML helps in hands-free initial evaluations of efficiency/accuracy of ML algorithms."

"The most valuable features are the machine learning tools, the support for Jupyter Notebooks, and the collaboration that allows you to share it across people."

"Fast training, memory-efficient DataFrame manipulation, well-documented, easy-to-use algorithms, ability to integrate with enterprise Java apps (through POJO/MOJO) are the main reasons why we switched from Spark to H2O."

"It is helpful, intuitive, and easy to use. The learning curve is not too steep."

Cons

"The main challenge with Amazon SageMaker is the integrations."

"The product must provide better documentation."

"The platform could be more accessible to users with basic coding skills, making it more intuitive and easier for beginners to use comfortably."

"The main challenge with Amazon SageMaker is the integrations."

"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."

"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."

"I had to create custom templates for labeling multi-data sets, such as text and images, which was time-consuming."

"While integration is available, there are concerns about how secure this integration is, particularly when exposing data to SageMaker."

More Amazon SageMaker cons

"It needs a drag and drop GUI like KNIME, for easy access to and visibility of workflows."

"The model management features could be improved."

"On the topic of model training and model governance, this solution cannot handle ten or twelve models running at the same time."

"H2O.ai can improve in areas like multimodal support and prompt engineering."

"The interpretability module has room for improvement. Also, it needs to improve its ability to integrate with other systems, like SageMaker, and the overall integration capability."

"It lacks the data manipulation capabilities of R and Pandas DataFrames. We would kill for dplyr offloading H2O."

"Referring to bullet-3 as well, H2O DataFrame manipulation capabilities are too primitive."

"I would like to see more features related to deployment."

Pricing and Cost Advice

"The support costs are 10% of the Amazon fees and it comes by default."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a six out of ten."

"SageMaker is worth the money for our use case."

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."

"On average, customers pay about $300,000 USD per month."

"The pricing is comparable."

"Amazon SageMaker is a very expensive product."

"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."

More Amazon SageMaker pricing and cost advice

"We have seen significant ROI where we were able to use the product in certain key projects and could automate a lot of processes. We were even able to reduce staff."

See which vendors are best for you

Use our free recommendation engine to learn which Data Science Platforms solutions are best for your needs.

See recommendations

861,390 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

19%

Computer Software Company

12%

Manufacturing Company

Educational Organization

Financial Services Firm

17%

Computer Software Company

14%

Manufacturing Company

10%

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

See all answers

What do you like most about Amazon SageMaker?

We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...

See all answers

What is your experience regarding pricing and costs for Amazon SageMaker?

The pricing is high, around an eight. However, SageMaker offers free trials for the first two months, allowing users to determine which features they need. It is considered value for money given it...

See all answers

What needs improvement with H2O.ai?

H2O.ai can improve in areas like multimodal support and prompt engineering. They are already working on updates and changes. Although I haven't explored all the new products they've added to their ...

See all answers

What is your primary use case for H2O.ai?

We mostly used the solution in the domain that I'm working. We had most of the use cases around chatbots and conversational BI.

See all answers

What advice do you have for others considering H2O.ai?

It is important to address data privacy concerns and ensure you're choosing the right vendor that meets your use case demands. Also, you may leave my name, Kashif, but please keep the company name ...

See all answers

Comparisons

Databricks vs Amazon SageMaker

Compared 12% of the time

Dataiku vs Amazon SageMaker

Compared 11% of the time

Azure OpenAI vs Amazon SageMaker

Compared 11% of the time

Google Vertex AI vs Amazon SageMaker

Compared 9% of the time

Cloudera Data Science Workbench vs Amazon SageMaker

Compared 2% of the time

More Amazon SageMaker Competitors

Dataiku vs H2O.ai

Compared 25% of the time

Databricks vs H2O.ai

Compared 19% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 17% of the time

KNIME vs H2O.ai

Compared 11% of the time

Altair RapidMiner vs H2O.ai

Compared 5% of the time

More H2O.ai Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

July 2025

Download Amazon SageMaker product report

Buyer's Guide

Data Science Platforms

June 2025

Download H2O.ai product report

Also Known As

AWS SageMaker, SageMaker

No data available

Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Web Services (AWS)

H2O is a fully open source, distributed in-memory machine learning platform with linear scalability. H2O’s supports the most widely used statistical & machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O also has an industry leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models. The H2O platform is used by over 14,000 organizations globally and is extremely popular in both the R & Python communities.

H2O.ai

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

poder.io, Stanley Black & Decker, G5, PWC, Comcast, Cisco

Buyer's Guide

Amazon SageMaker vs. H2O.ai

June 2025

Free Report: Amazon SageMaker vs. H2O.ai

Find out what your peers are saying about Amazon SageMaker vs. H2O.ai and other solutions. Updated: June 2025.

DOWNLOAD NOW

861,390 professionals have used our research since 2012.

See our Amazon SageMaker vs. H2O.ai report.

See our list of best Data Science Platforms vendors.

We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.