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 #5 with an average rating of 7.6, while H2O.ai is ranked #22. Amazon Web Services (AWS) holds a 7.7% mindshare in Data Science Platforms, compared to H2O.ai’s 1.5% mindshare. Additionally, 88% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of H2O.ai users who would recommend it.

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Executive Summary

We performed a comparison between Amazon SageMaker and H2O.ai based on real PeerSpot user reviews.

Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed Amazon SageMaker vs. H2O.ai Report (Updated: October 2024).

Buyer's Guide

Amazon SageMaker vs. H2O.ai

October 2024

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Helped 814,649 peers since 2012

Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of November 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 2.8%, up from 2.6% compared to the previous year. The mindshare of Amazon SageMaker is 7.7%, down from 10.4% compared to the previous year. The mindshare of H2O.ai is 1.5%, up from 1.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

AbakarAhmat

Software Engineer at a university with 10,001+ employees

Sep 21, 2023

Enhancing survey analysis that provides valued insightfulness

I use it to analyze questionnaire surveys related to a product, solution, or application, such as open data services, which I provide to consumers and end-users. These surveys contain evaluation assessments, and I use SPSS to analyze the responses The most valuable feature is its robust…

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Natu Lauchande

Tech Lead - Sanlam Fintech Cluster - Data,ML,AI Eng. at Sanlam

Feb 27, 2024

Easy to use and manage, but the documentation does not have a lot of information

We use the product for deploying machine learning models. We use it for the machine learning model development process We're currently implementing a project on a cross-selling model. It is like a standard XGBoost model. I’m evaluating the tool to see whether it will improve the workflow.…

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Rahul Koduru

Director of Data Engineering at Transamerica

Dec 11, 2018

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

One example, we are able to automate life insurance. We have to underwrite policies. When somebody applies for a policy, we take their blood, then assign them a risk: substandard, standard, preferred, etc. Depending on this, we price our products. Usually the process is that you take the blood, then it goes to a lab and we get the lab results back, then an underwriter takes a look at the lab results. This is usually done in a two week time frame to get a rating. We were able to build models to automate all of this, and now, it happens in real-time. Somebody can apply online and get issued a policy right away.

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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

"SPSS is quite robust and quicker in terms of providing you the output."

"Capability analysis is one of the main and valuable functions. We also do some hypothesis testing in Minitab and summary stats. These are the functions that we find very useful."

"The best part is that they have an algorithm handbook, so you can open it up and understand how it works, and if it is useful, this is very important."

"Most of the product features are good but I particularly like the linear regression analysis."

"The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can into multidimensional setup space. It's the multidimensional space facility that is most useful."

"Since we are using the software as a statistical tool, I would say the best aspects of it are the regression and segmentation capabilities. That said, I've used it for all sorts of things."

"The software offers consistency across multiple research projects helping us with predictive analytics capabilities."

"IBM SPSS Statistics depends on AI."

More IBM SPSS Statistics pros

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

"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."

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

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

"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."

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

"The technical support of the tool was good."

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

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."

"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."

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

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

Cons

"I think the visualization and charting should be changed and made easier and more effective."

"The design of the experience can be improved."

"Most of the package will give you the fixed value, or the p-value, without an explanation as to whether it it significant or not. Some beginners might need not just the results, but also some explanation for them."

"Better documentation on how to use macros."

"Technical support needs some improvement, as they do not respond as quickly as we would like."

"The statistics should be more self-explanatory with detailed automated reports."

"I feel that when it comes to conducting multiple analyses, there could be more detailed information provided. Currently, the software gives a summary and an overview, but it would be beneficial to have specific details for each product or variable."

"The solution needs more planning tools and capabilities."

More IBM SPSS Statistics cons

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

"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."

"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."

"Lacking in some machine learning pipelines."

"The solution requires a lot of data to train the model."

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

"SageMaker would be improved with the addition of reporting services."

"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."

More Amazon SageMaker cons

"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."

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

"The model management features could be improved."

"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."

Pricing and Cost Advice

"Our licence is on a yearly renewal basis. While pricing is not the primary concern in our evaluation, as products are assessed by whether they can meet our user needs and expertise, the cost can be a limiting factor in the number of licences we procure."

"The price of IBM SPSS Statistics could improve."

"While the pricing of the product may be higher, the accompanying service and features justify the investment."

"The price of this solution is a little bit high, which was a problem for my company."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

"We think that IBM SPSS is expensive for this function."

"More affordable training for new staff members."

"SPSS is an expensive piece of software because it's incredibly complex and has been refined over decades, but I would say it's fairly priced."

More IBM SPSS Statistics 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."

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

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

"The pricing is comparable."

"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."

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

"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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814,649 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

16%

University

10%

Computer Software Company

Manufacturing Company

Financial Services Firm

18%

Educational Organization

14%

Computer Software Company

11%

Manufacturing Company

Financial Services Firm

19%

Computer Software Company

11%

Manufacturing Company

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about IBM SPSS Statistics?

The software offers consistency across multiple research projects helping us with predictive analytics capabilities.

See all answers

What is your experience regarding pricing and costs for IBM SPSS Statistics?

While the pricing of the product may be higher, the accompanying service and features justify the investment. However...

See all answers

What needs improvement with IBM SPSS Statistics?

In some cases, the product takes time to load a large dataset. They could improve this particular area.

See all answers

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 designe...

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 Cha...

See all answers

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

The license cost for Amazon SageMaker ranges between seven thousand to fifteen thousand dollars per month depending o...

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Comparisons

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Compared 7% of the time

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Compared 5% of the time

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Compared 20% of the time

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Compared 19% of the time

Microsoft Azure Machine Learning Studio vs H2O.ai

Compared 15% of the time

KNIME vs H2O.ai

Compared 11% of the time

SAS Visual Analytics vs H2O.ai

Compared 5% of the time

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Product Reports

Buyer's Guide

IBM SPSS Statistics

October 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Amazon SageMaker

November 2024

Download Amazon SageMaker product report

Buyer's Guide

Data Science Platforms

October 2024

Download H2O.ai product report

Also Known As

SPSS Statistics

AWS SageMaker, SageMaker

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IBM

Amazon Web Services (AWS)

H2O.ai

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

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

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA

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

October 2024

Free Report: Amazon SageMaker vs. H2O.ai

Find out what your peers are saying about Amazon SageMaker vs. H2O.ai and other solutions. Updated: October 2024.

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