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 #2 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 6.5% mindshare in Data Science Platforms, compared to H2O.ai’s 1.7% 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

5,230 Views
1,674 Comparison Views

91% willing to recommend

H2O.ai

Read 8 H2O.ai reviews

1,152 Views
379 Comparison Views

100% willing to recommend

Amazon SageMaker

H2O.ai

Comparison Buyer's Guide

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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: June 2025).

Buyer's Guide

Amazon SageMaker vs. H2O.ai

June 2025

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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

2nd

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 June 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.5%, down from 9.6% compared to the previous year. The mindshare of H2O.ai is 1.7%, 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.

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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

"We've had no problems with SageMaker's stability."

"I appreciate the ease of use in Amazon SageMaker."

"They offer insights into everyone making calls in my organization."

"The tool makes our ML model development a bit more efficient because everything is in one environment."

"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."

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

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

"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."

More Amazon SageMaker pros

"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."

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

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

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

Cons

"The solution is complex to use."

"The product must provide better documentation."

"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."

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

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

"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."

"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."

"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."

More Amazon SageMaker cons

"The model management features could be improved."

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

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

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

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

"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."

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

Pricing and Cost Advice

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

"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."

"I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."

"The pricing could be better, especially for querying. The per-query model feels expensive."

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

"The cost offers a pay-as-you-go pricing model. It depends on the instance that you do."

"The solution is relatively cheaper."

"The product is expensive."

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

19%

Computer Software Company

12%

Manufacturing Company

Educational Organization

Financial Services Firm

19%

Computer Software Company

13%

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...

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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

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Product Reports

Buyer's Guide

Amazon SageMaker

June 2025

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Buyer's Guide

Data Science Platforms

June 2025

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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.

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