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 7.7% 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

7,315 Views
5,868 Comparison Views

91% willing to recommend

H2O.ai

Read 8 H2O.ai reviews

1,366 Views
978 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: January 2025).

Buyer's Guide

Amazon SageMaker vs. H2O.ai

January 2025

Download the complete report

Helped 831,158 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 January 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 7.7%, down from 10.0% compared to the previous year. The mindshare of H2O.ai is 1.5%, down from 1.6% 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.

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

"Allows you to create API endpoints."

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

"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 feature I found most valuable is the data catalog, as it assists with the lineage of data through the preparation pipeline."

"SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project."

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

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

"The evolution from SageMaker Classic to SageMaker Studio, particularly the UI part of Studio, is commendable."

More Amazon SageMaker pros

"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."

"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."

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

"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."

Cons

"One area for improvement is the pricing, which can be quite high."

"The model repository is a concern as models are stored on a bucket and there's an issue with versioning."

"Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker."

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

"One area for improvement is the pricing, which can be quite high."

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

"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."

"Amazon might need to emphasize its capabilities in generative models more effectively."

More Amazon SageMaker cons

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

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

"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."

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

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

"The model management features could be improved."

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

Pricing and Cost Advice

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

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

"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."

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

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

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

"Amazon SageMaker is a very expensive product."

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

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

831,158 professionals have used our research since 2012.

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...

See all answers

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

Databricks vs Amazon SageMaker

Compared 26% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

Google Vertex AI vs Amazon SageMaker

Compared 10% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

SAS Visual Analytics vs Amazon SageMaker

Compared 2% of the time

More Amazon SageMaker Competitors

Databricks vs H2O.ai

Compared 22% of the time

Dataiku vs H2O.ai

Compared 17% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 14% of the time

KNIME vs H2O.ai

Compared 12% of the time

IBM Watson Studio vs H2O.ai

Compared 5% of the time

More H2O.ai Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

January 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

Learn More

Amazon Web Services (AWS)

H2O.ai

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.

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.

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.

DOWNLOAD NOW

831,158 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.