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Amazon SageMaker vs IBM SPSS Statistics comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

Review summaries and opinions

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

ROI

Sentiment score
7.3
Amazon SageMaker delivers high ROI by reducing costs and time, often providing returns multiple times the initial investment.
Sentiment score
5.6
IBM SPSS Statistics provides significant ROI by improving data analysis efficiency, saving time and costs with user-friendly features.
The return on investment varies by use case and offers significant value in revenue increases and cost saving capabilities, especially in real time fraud detection and targeted advertisements.
 

Customer Service

Sentiment score
7.2
Amazon SageMaker support is generally praised, but service quality varies; premium customers receive better, more responsive assistance.
Sentiment score
6.3
IBM SPSS Statistics customer service is generally polite and effective, but some users request better initial technical knowledge.
The technical support from AWS is excellent.
The response time is generally swift, usually within seven to eight hours.
The support is very good with well-trained engineers.
 

Scalability Issues

Sentiment score
7.6
Amazon SageMaker offers scalability and adaptability across enterprises, but GPU limitations and user skills impact its overall efficiency.
Sentiment score
6.3
IBM SPSS Statistics efficiently handles moderate data but struggles with large datasets, with mixed opinions on its scalability.
It works very well with large data sets from one terabyte to fifty terabytes.
The availability of GPU instances can be a challenge, requiring proper planning.
Amazon SageMaker is scalable and works well from an infrastructure perspective.
 

Stability Issues

Sentiment score
7.8
Amazon SageMaker is stable with high reliability, especially when properly configured, and minor glitches do not significantly impact performance.
Sentiment score
7.4
IBM SPSS Statistics is stable and reliable, efficiently handling data with adequate hardware but requires improvement for large databases.
There are issues, but they are easily detectable and fixable, with smooth error handling.
I rate the stability of Amazon SageMaker between seven and eight.
 

Room For Improvement

Users seek better pricing, interface, integration, documentation, AI, dataset support, security, serverless options, and AWS collaboration.
IBM SPSS needs better visualization, user interface, support, and integration, with modern features, automation, and adaptable algorithms to remain competitive.
Both SageMaker and Lambda are powerful tools, and combining their capabilities could be beneficial.
This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background.
Integration of the latest machine learning models like the new Amazon LLM models could enhance its capabilities.
I'm unsure if SPSS has a commercial offering for big servers, unlike KNIME, which does.
 

Setup Cost

Amazon SageMaker offers flexible, competitive pricing but can be costly, with value varying by user, plus available discounts.
IBM SPSS Statistics is a premium-priced solution with extensive features, offset by educational discounts and perceived affordability challenges.
The cost for small to medium instances is not very high.
The pricing can be up to eight or nine out of ten, making it more expensive than some cloud alternatives yet more economical than on-premises setups.
For a single user, prices might be high yet could be cheaper for user-managed services compared to AWS-managed services.
 

Valuable Features

Amazon SageMaker offers comprehensive tools for end-to-end machine learning, including model deployment, scalability, and user-friendly features.
IBM SPSS Statistics combines advanced analytics with a user-friendly interface, handling large datasets and supporting predictive modeling efficiently.
SageMaker supports building, training, and deploying AI models from scratch, which is crucial for my ML project.
SageMaker is fully managed, offers high availability, flexibility with TensorFlow, PyTorch, and MXNet, and comes with pre-trained algorithms for forecasting, anomaly detection, and more.
These features facilitate rapid development and deployment of AI applications.
I mainly used it for cross tabs, correlation, regression, chi-squared tests, and similar analyses often seen in published papers.
 

Categories and Ranking

Amazon SageMaker
Ranking in Data Science Platforms
3rd
Average Rating
7.8
Reviews Sentiment
7.1
Number of Reviews
37
Ranking in other categories
AI Development Platforms (5th)
IBM SPSS Statistics
Ranking in Data Science Platforms
8th
Average Rating
8.0
Reviews Sentiment
6.5
Number of Reviews
39
Ranking in other categories
Data Mining (3rd)
 

Mindshare comparison

As of July 2025, in the Data Science Platforms category, the mindshare of Amazon SageMaker is 6.2%, down from 9.3% compared to the previous year. The mindshare of IBM SPSS Statistics is 2.8%, down from 2.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

Saurabh Jaiswal - PeerSpot reviewer
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.
Laurence Moseley - PeerSpot reviewer
Delivers reliable results for academic research and keeps you close to your data.
SPSS is perfectly adequate if all you want are some results. If you only need the results, you do not require the trail of evidence on how you obtained those results. I mainly used it for cross tabs, correlation, regression, chi-squared tests, and similar analyses often seen in published papers; I used it for my papers as well. For wider uses I find Knime keeps me in touch with my data, however much I transform them.
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Top Industries

By visitors reading reviews
Financial Services Firm
19%
Computer Software Company
12%
Manufacturing Company
8%
Educational Organization
6%
Financial Services Firm
17%
Computer Software Company
8%
Manufacturing Company
8%
University
8%
 

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...
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...
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...
What do you like most about IBM SPSS Statistics?
The software offers consistency across multiple research projects helping us with predictive analytics capabilities.
What is your experience regarding pricing and costs for IBM SPSS Statistics?
SPSS is horrendously expensiver. On a laptop Knime is free of charge (Windows, Mac, Linux)
What needs improvement with IBM SPSS Statistics?
Better guidance both in producing programs and interpreting their output.
 

Also Known As

AWS SageMaker, SageMaker
SPSS Statistics
 

Overview

 

Sample Customers

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit
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
Find out what your peers are saying about Amazon SageMaker vs. IBM SPSS Statistics and other solutions. Updated: June 2025.
861,524 professionals have used our research since 2012.