Amazon SageMaker vs IBM SPSS Modeler comparison

Amazon Web Services (AWS) and IBM are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.0, while IBM is ranked #13 with an average rating of 6.8. Amazon Web Services (AWS) holds a 6.9% mindshare in Data Science Platforms, compared to IBM’s 2.4% mindshare. Additionally, 91% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 86% of IBM users who would recommend it.

Amazon SageMaker

Read 37 Amazon SageMaker reviews

6,112 Views
4,892 Comparison Views

91% willing to recommend

IBM SPSS Modeler

Read 39 IBM SPSS Modeler reviews

1,793 Views
1,459 Comparison Views

86% willing to recommend

Amazon SageMaker

IBM SPSS Modeler

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 5, 2024

IBM SPSS Modeler and Amazon SageMaker are competitive in the data analytics and machine learning platforms category. SPSS Modeler may have the upper hand in providing comprehensive analytics, whereas SageMaker offers broader utility in machine learning environments, especially with cloud integration.

Features: IBM SPSS Modeler includes advanced statistical analysis tools, intuitive data mining, and data manipulation features. It provides a point-and-click interface that simplifies complex analytical tasks. Amazon SageMaker offers a robust machine learning suite, integration with AWS infrastructure, and capabilities for building, training, and deploying models at scale.

Room for Improvement: IBM SPSS Modeler could improve its cloud deployment capabilities and expand integration options with modern data platforms. It might also benefit from enhancing its visualization features. Amazon SageMaker could improve by providing more extensive pre-built models, simplifying onboarding for complex tasks, and enhancing offline capabilities for hybrid environments.

Ease of Deployment and Customer Service: Amazon SageMaker offers seamless deployment within the AWS ecosystem, which is advantageous for large-scale projects. It provides comprehensive support with extensive documentation and active channels. IBM SPSS Modeler supports multiple platforms but may face challenges in cloud environments. Customer service is reliable but lacks the depth found in SageMaker's support structure.

Pricing and ROI: IBM SPSS Modeler usually requires higher upfront costs due to its specialized analytics capabilities, leading to solid ROI for dedicated analytical tasks. In contrast, Amazon SageMaker presents a flexible pricing model that aligns with usage patterns, potentially offering better cost-effectiveness for projects with varying demands. SageMaker's cloud synergy can contribute to greater overall value.

To learn more, read our detailed Amazon SageMaker vs. IBM SPSS Modeler Report (Updated: April 2025).

Buyer's Guide

Amazon SageMaker vs. IBM SPSS Modeler

April 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

3rd

Average Rating

7.8

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

AI Development Platforms (5th)

IBM SPSS Modeler

Ranking in Data Science Platforms

13th

Average Rating

8.0

Reviews Sentiment

6.6

Number of Reviews

Ranking in other categories

Data Mining (4th)

Mindshare comparison

As of May 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.9%, down from 9.7% compared to the previous year. The mindshare of IBM SPSS Modeler is 2.4%, down from 2.7% 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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PeterHuo

Application Architect at Canada Border Services Agency

Good tool for extracting data from data warehouses, creating streams, and manipulating logic to extract final data

There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the server due to memory leaks. This performance problem bothers people. The performance issue seems to be related to the server. We design streams on the client and submit them to the server, which generates a large SQL statement. There are two potential bottlenecks: one in the server and another in data extraction. I'm unsure about the exact mechanics of data splitting when fetching from the database. When streams become larger, performance bottlenecks may occur in the IBM SPSS Modeler server or the database. Sometimes the server crashes and needs to be restarted to release memory on both sides. I'm not sure exactly where the problem is caused, as I focus on stream design rather than server issues. The problem could be on the IBM SPSS Modeler server and database.

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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

"SageMaker is a comprehensive platform where I can perform all machine learning activities."

"The technical support from AWS is excellent."

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

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

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

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

"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 these models, making accessing them convenient as needed."

"The most valuable features include the ML operations that allow for designing, deploying, testing, and evaluating models."

More Amazon SageMaker pros

"IBM was chosen because of usability. It's point and click, whereas the other out-of-the box-solution, or open-source solutions, require full-on programming and a much higher skill level."

"I think the code modeling features are the most valuable and without the need to write a code back with many different possibilities to choose from. And the second one is linked to the activity of the data preparation."

"We had an IBM Guardium service contract where we used one of their resources to help us develop our prototype. It was a good experience, but they were helpful and responsive."

"We have a local representative who specializes in SPSS. He will help us do the PoC."

"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."

"The quality is very good."

"It is pretty scalable."

"It will scale up to anything we need."

More IBM SPSS Modeler pros

Cons

"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."

"Lacking in some machine learning pipelines."

"There are other better solutions for large data, such as Databricks."

"One area where Amazon SageMaker could improve is its pricing. The high costs can drive companies to explore other cloud options. Additionally, while generally good, the updates sometimes come with bugs, and the documentation could be much better. More examples and clearer guidance would be helpful."

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

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

"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."

"The product must provide better documentation."

More Amazon SageMaker cons

"The standard package (personal) is not supported for database connection."

"We would like to see better visualizations and easier integration with Cognos Analytics for reporting."

"C&DS will not meet our scalability needs."

"When I used it in the office, back in the day, we did have some stability issues. Sometimes it just randomly crashed and we couldn't get good feedback. But when I use it for my own stuff now I don't have any problems."

"Unstructured data is not appropriate for SPSS Modeler."

"I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it."

"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."

"It's not as user friendly as it could be."

More IBM SPSS Modeler cons

Pricing and Cost Advice

"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."

"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."

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

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

"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."

"Databricks solution is less costly than Amazon SageMaker."

"The product is expensive."

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

More Amazon SageMaker pricing and cost advice

"It is an expensive product."

"It got us a good amount of money with quick and efficient modeling."

"The scalability was kind of limited by our ability to get other people licenses, and that was usually more of a financial constraint. It's expensive, but it's a good tool."

"Its price is okay for a company, but for personal use, it is considered somewhat expensive."

"I am using the free version of IBM SPSS Modeler, it is the educational edition version."

"If you are in a university and the license is free then you can use the tool without any charges, which is good."

"$5,000 annually."

"It is a huge increase to time savings."

More IBM SPSS Modeler pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

19%

Educational Organization

11%

Computer Software Company

11%

Manufacturing Company

Financial Services Firm

13%

Educational Organization

11%

University

Computer Software 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 do you like most about IBM SPSS Modeler?

Compared to other tools, the product works much easier to analyze data without coding.

See all answers

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

The government has funds and a budget, it's hard to say if it's expensive or cheap. In Canada, they have a yearly budget. They used to encourage people to use the modeler for development. If ten us...

See all answers

What needs improvement with IBM SPSS Modeler?

See all answers

Comparisons

Databricks vs Amazon SageMaker

Compared 15% of the time

Azure OpenAI vs Amazon SageMaker

Compared 12% of the time

Dataiku vs Amazon SageMaker

Compared 11% of the time

Google Vertex AI vs Amazon SageMaker

Compared 9% of the time

Domino Data Science Platform vs Amazon SageMaker

Compared 7% of the time

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Microsoft Power BI vs IBM SPSS Modeler

Compared 27% of the time

IBM SPSS Statistics vs IBM SPSS Modeler

Compared 20% of the time

KNIME vs IBM SPSS Modeler

Compared 16% of the time

Altair RapidMiner vs IBM SPSS Modeler

Compared 8% of the time

SAS Analytics vs IBM SPSS Modeler

Compared 2% of the time

More IBM SPSS Modeler Competitors

Product Reports

Buyer's Guide

Amazon SageMaker

April 2025

Download Amazon SageMaker product report

Buyer's Guide

IBM SPSS Modeler

April 2025

Download IBM SPSS Modeler product report

Also Known As

AWS SageMaker, SageMaker

SPSS Modeler

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)

IBM SPSS Modeler is an extensive predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and the enterprise. By providing a range of advanced algorithms and techniques that include text analytics, entity analytics, decision management and optimization, SPSS Modeler can help you consistently make the right decisions from the desktop or within operational systems.

Buy
https://www.ibm.com/products/spss-modeler/pricing

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https://www.ibm.com/account/reg/us-en/signup?formid=urx-19947

IBM

Sample Customers

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

Reisebªro Idealtours GmbH, MedeAnalytics, Afni, Israel Electric Corporation, Nedbank Ltd., DigitalGlobe, Vodafone Hungary, Aegon Hungary, Bureau Veritas, Brammer Group, Florida Department of Juvenile Justice, InSites Consulting, Fortis Turkey

Buyer's Guide

Amazon SageMaker vs. IBM SPSS Modeler

April 2025

Free Report: Amazon SageMaker vs. IBM SPSS Modeler

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