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 7.9, while H2O.ai is ranked #20 with an average rating of 7.0. Amazon Web Services (AWS) holds a 7.6% mindshare in Data Science Platforms, compared to H2O.ai’s 1.5% 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 36 Amazon SageMaker reviews

7,026 Views
5,578 Comparison Views

91% willing to recommend

H2O.ai

Read 8 H2O.ai reviews

1,304 Views
952 Comparison Views

100% willing to recommend

Amazon SageMaker

H2O.ai

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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: January 2025).

Buyer's Guide

Amazon SageMaker vs. H2O.ai

January 2025

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Helped 838,640 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 (4th)

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 (6th)

Mindshare comparison

As of February 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.6%, down from 9.9% compared to the previous year. The mindshare of H2O.ai is 1.5%, down from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Hemant Paralkar

Lead Consultant at Saama

Improves team collaboration with advanced feature sharing but needs a better user experience

Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background. Additionally, dealing with frequent UI updates can be challenging, especially for infrastructure architects like myself. It involves effort to migrate to new UIs, making the updates not seamless. User auditing requires enhancements as tracking operations performed by users can be difficult due to dynamic IP validation and role propagation.

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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…

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Quotes from Members

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

Pros

"They are doing a good job of evolving."

"The most valuable features are the ability to store artifacts and gather reports and measures from experiments."

"We were able to use the product to automate processes."

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

"Amazon SageMaker is highly valuable for managing ML workloads. It connects to AWS cloud resources, making it easy to deploy algorithms and collaborate using tools like GitLab. It offers a wide range of Python libraries and other necessary tools for modelling and algorithms."

"The most valuable feature of Amazon SageMaker for me is the model deployment service."

"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."

"The technical support of the tool was good."

More Amazon SageMaker pros

"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."

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

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

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

"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."

Cons

"The user interface (UI) and user experience (UX) of SageMaker and AWS, in general, need improvement as they are not intuitive and require substantial time to learn how to use specific services."

"AI is a new area and AWS needs to have an internship training program available."

"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."

"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."

"The solution needs to be cheaper since it now charges per document for extraction."

"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."

"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker."

"Amazon SageMaker can make it simpler to manage the data flow from start to finish, such as by integrating data, usingthe machine, and deploying models. This process could be more user-friendly compared to other tools. I would also like to improve integration with Bedrock and the LLM connection for AWS."

More Amazon SageMaker cons

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

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

"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."

"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."

Pricing and Cost Advice

"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."

"I would rate the solution's price a ten out of ten since it is very high."

"There is no license required for the solution since you can use it on demand."

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

"The tool's pricing is reasonable."

"In terms of pricing, I'd also rate it ten out of ten because it's been beneficial compared to other solutions."

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

"The solution is relatively cheaper."

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."

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Top Industries

By visitors reading reviews

Financial Services Firm

18%

Educational Organization

14%

Computer Software Company

11%

Manufacturing Company

Financial Services Firm

21%

Computer Software Company

11%

Manufacturing Company

10%

Energy/Utilities Company

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...

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

Before deploying SageMaker, I reviewed the pricing, especially for notebook instances. The cost for small to medium instances is not very high.

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

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Compared 8% of the time

SAS Visual Analytics vs Amazon SageMaker

Compared 2% of the time

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Compared 22% of the time

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Compared 18% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 13% of the time

KNIME vs H2O.ai

Compared 12% of the time

RapidMiner vs H2O.ai

Compared 5% of the time

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Product Reports

Buyer's Guide

Amazon SageMaker

February 2025

Download Amazon SageMaker product report

Buyer's Guide

Data Science Platforms

January 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

January 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: January 2025.

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