IBM SPSS Modeler vs KNIME vs SAS Analytics comparison

IBM and Knime are both solutions in the Data Mining category. IBM is ranked #4 with an average rating of 6.8, while Knime is ranked #1 with an average rating of 7.9. IBM holds a 18.1% mindshare in DM, compared to Knime’s 24.7% mindshare. Additionally, 86% of IBM users are willing to recommend the solution, compared to 94% of Knime users who would recommend it.

IBM SPSS Modeler

Read 39 IBM SPSS Modeler reviews

968 Views
781 Comparison Views

86% willing to recommend

KNIME

Read 60 KNIME reviews

1,672 Views
1,205 Comparison Views

94% willing to recommend

SAS Analytics

Read 11 SAS Analytics reviews

551 Views
335 Comparison Views

94% willing to recommend

IBM SPSS Modeler

KNIME

SAS Analytics

Comparison Buyer's Guide

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Executive Summary

We performed a comparison between IBM SPSS Modeler, KNIME, and SAS Analytics based on real PeerSpot user reviews.

Find out what your peers are saying about Knime, Weka, IBM and others in Data Mining.

To learn more, read our detailed Data Mining Report (Updated: May 2025).

Buyer's Guide

Data Mining

May 2025

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Helped 856,873 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:

Mindshare comparison

As of June 2025, in the Data Mining category, the mindshare of IBM SPSS Modeler is 18.1%, up from 16.4% compared to the previous year. The mindshare of KNIME is 24.7%, down from 27.2% compared to the previous year. The mindshare of SAS Analytics is 6.8%, down from 8.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining

Featured Reviews

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.

Read full review

Laurence Moseley

Emeritus Professor of Health Services Research at University of South Wales

Has a drag-and-drop interface and AI capabilities

It's difficult to pinpoint one single feature because KNIME has so many. For starters, it's very easy to learn. You can get started with just a one-hour video. The drag-and-drop interface makes it user-friendly. For example, if you want to read an Excel file, drag the "read Excel file" node from the repository, configure it by specifying the file location, and run it. This gives you a table with all your data. Next, you can clean the data by handling missing values, selecting specific columns you want to analyze, and then proceeding with your analysis, such as regression or correlation. KNIME has over 4,500 nodes available for download. In addition, KNIME offers various extensions. For instance, the text processing extension allows you to process text data efficiently. It's more powerful than other tools like NVivo and Palantir. KNIME also has AI capabilities. If you're unsure about the next step, the AI assistant can suggest the most frequently used nodes based on your previous work. Another valuable feature is the integration with Python. If you need to perform a task that requires Python, you can simply add a Python node, write the necessary code,

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AltanAtabarut

Solution Consulting, Growth, Analytics at Akinon

Good interactive reports and dashboards but expensive to run

You need to constantly jump from data prep to visualization, then back to data for fixing quality, enriching, and visualizing again. Then you start modeling and need to switch back and forth again. Eventually, you have to democratize the results so that anyone can look and interactively question the underlying insights. For all of these purposes - data to BI or data to AI to BI - it is never a linear one way relationship; it's rather a closed loop for continuous analysis monitoring and sharing. If used this way, SAS Visual analytics can enable continuous improvement and innovation in a corporation.

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

"It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it"

"A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly."

"It gives you a GUI interface, which is a lot more user-friendly and easier to use compared to writing R scripts or Python."

"It will scale up to anything we need."

"Automation is great and this product is very organized."

"It scales. I have not run into any challenges where it will not perform."

"So far, the stability has been rock solid."

"It is pretty scalable."

More IBM SPSS Modeler pros

"All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function."

"I am impressed by the modularity and reusability in KNIME, especially the ability to make small adjustments to object configurations."

"What I like the most is that it works almost out of the box with Random Forest and other Forest nodes."

"It is a stable solution...It is a scalable solution."

"It allows for a user-friendly approach where you can simply drag and drop elements to create your model, which is a convenient and effective idea."

"KNIME is fast and the visualization provides a lot of clarity. It clarifies your thinking because you can see what's going on with your data."

"Clear view of the data at every step of ETL process enables changing the flow as needed."

"It is possible to configure the system to effectively manage memory and space requirements."

More KNIME pros

"I use SAS daily to analyze data, produce reports, and other outputs."

"SAS Business Intelligence is well-suited for our large corporation. We have demand for scalable and reliable insights into information which is housed in our large systems."

"I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile app."

"It has improved the level of efficacy and validity of our reports."

"The team immediately resolves the issues."

"They have provided virtually everything we have needed to accomplish our task, as well as continuously improving our accuracy."

"Modeling ones and figures, such as PROC LIFETEST, PROC LOGISTICS, PROC GPLOT. PROC FREQ and PROC MEANS, are also among the valuable features."

"I use it to replicate our entire financial system to verify/duplicate calculations."

More SAS Analytics pros

Cons

"I can say the solution is outdated."

"The forecasting could be a bit easier."

"I would like better integration into the Weather Company solution. I have raised a couple of concerns about this integration and having more time series capabilities."

"I would not rate the technical support very well. The technicians have accents. When you do find someone, it is very hard to get somebody able to answer the technical questions."

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

"The time series should be improved."

"Dimension reduction should be classified separately."

"The integration with sources and visualisation needs some improvement. The scalability needs improvement."

More IBM SPSS Modeler cons

"I wish there were more video training resources for KNIME. The current videos are very short, and most learning is text-based. Longer training sessions would be helpful, especially for complex flowchart use cases. Webinars focusing on starting projects and analyzing data would also be beneficial."

"KNIME can improve by adding more automation tools in the query, similar to UiPath or Blue Prism. It would make the data collection and cleanup duties more versatile."

"KNIME's documentation is not strong."

"There should be better documentation and the steps should be easier."

"The predefined workflows could use a bit of improvement."

"There are a lot of tools in the product and it would help if they were grouped into classes where you can select a function, rather than a specific tool."

"Sometimes, we needed more space to handle larger operations, especially since our machines had limited space and memory due to Kubernetes clusters."

"The program is not fit for handling very large files or databases (greater than 1GB); it gets too slow and has a tendency to crash easily."

More KNIME cons

"The installation could also be easier, and the price could be better."

"Once a SAS figure is produced one would like to modify things, such as titles, legends, and incorporate risk sets as a footer on the plots."

"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."

"They could enhance the AI capabilities of the product."

"I would like to see their interface to R added to either Base SAS or SAS Analytics."

"The graphing and visualization features could be enhanced, in my opinion. I would especially stress improving the visualization capabilities."

"The natural language querying and automated preparation of dashboards should be improved."

"The training for SAS Business Intelligence is often difficult to arrange. It is often cancelled due to not enough people being enrolled."

More SAS Analytics cons

Pricing and Cost Advice

"It is an expensive product."

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

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

"It is a huge increase to time savings."

"Having in mind all four tools from Garner’s top quadrant, the pricing of this tool is competitive and it reflects the quality that it offers."

"When you are close to end of quarter, IBM and its partners can get you 60% to 70% discounts, so literally wait for the last day of the quarter for the best prices. You may feel like you are getting robbed if you can't receive a good discount."

"$5,000 annually."

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

More IBM SPSS Modeler pricing and cost advice

"The price for Knime is okay."

"It is free of cost. It is GNU licensed."

"I use the open-source version."

"I use the tool's free version."

"KNIME assets are stand alone, as the solution is open source."

"KNIME is a cost-effective solution because it’s free of cost."

"We're using the free academic license just locally. I went for KNIME because they have a free academic license."

"KNIME is free as a stand-alone desktop-based platform but if you want to get a KNIME server then you can find the cost on their website."

More KNIME pricing and cost advice

"It is relatively expensive. It is not an easy software to afford."

"The cost for SAS Business Intelligence can prove to be a little prohibitive."

"Prices were comparable with alternative solutions."

"Setup costs were quite reasonable."

"Licensing was rather straightforward."

"I rate the product’s pricing a one out of ten."

"Our licensing covers the usage for around 50 data analysts."

"I think that the cost-benefit ratio is okay."

More SAS Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Financial Services Firm

12%

Educational Organization

11%

University

Computer Software Company

Financial Services Firm

12%

Manufacturing Company

11%

Computer Software Company

University

Financial Services Firm

22%

Computer Software Company

10%

Insurance Company

Educational Organization

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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 bud...

See all answers

What needs improvement with IBM SPSS Modeler?

There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the ser...

See all answers

What do you like most about KNIME?

Since KNIME is a no-code platform, it is easy to work with.

See all answers

What is your experience regarding pricing and costs for KNIME?

I rate the product’s pricing a seven out of ten, where one is cheap and ten is expensive.

See all answers

What needs improvement with KNIME?

I have seen the potential to interact with Python, which is currently a bit limited. I am interested in the newer ver...

See all answers

What do you like most about SAS Analytics?

I like that it is quickly embedding interactive reports and dashboards into a website, Outlook Mail, or even a mobile...

See all answers

What needs improvement with SAS Analytics?

SAS Analytics could improve in areas such as handling stress tests and workloads, as well as predicting financial mov...

See all answers

What is your primary use case for SAS Analytics?

I use SAS Analytics ( /products/sas-analytics-reviews ) in my work to analyze data, such as selecting credits for tra...

See all answers

Comparisons

Microsoft Power BI vs IBM SPSS Modeler

Compared 26% of the time

IBM SPSS Statistics vs IBM SPSS Modeler

Compared 21% of the time

Altair RapidMiner vs IBM SPSS Modeler

Compared 8% of the time

Alteryx vs IBM SPSS Modeler

Compared 5% of the time

Databricks vs IBM SPSS Modeler

Compared 2% of the time

More IBM SPSS Modeler Competitors

Altair RapidMiner vs KNIME

Compared 27% of the time

Dataiku vs KNIME

Compared 15% of the time

Microsoft Power BI vs KNIME

Compared 14% of the time

Alteryx vs KNIME

Compared 8% of the time

Amazon SageMaker vs KNIME

Compared 1% of the time

More KNIME Competitors

IBM SPSS Statistics vs SAS Analytics

Compared 20% of the time

Weka vs SAS Analytics

Compared 16% of the time

Oracle Advanced Analytics vs SAS Analytics

Compared 14% of the time

More SAS Analytics Competitors

Product Reports

Buyer's Guide

IBM SPSS Modeler

June 2025

Download IBM SPSS Modeler product report

Buyer's Guide

KNIME

June 2025

Download KNIME product report

Buyer's Guide

Data Mining

May 2025

Download SAS Analytics product report

Also Known As

SPSS Modeler

KNIME Analytics Platform

No data available

Overview

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

KNIME is an open-source analytics software used for creating data science that is built on a GUI based workflow, eliminating the need to know code. The solution has an inherent modular workflow approach that documents and stores the analysis process in the same order it was conceived and implemented, while ensuring that intermediate results are always available.

KNIME supports Windows, Linux, and Mac operating systems and is suitable for enterprises of all different sizes. With KNIME, you can perform functions ranging from basic I/O to data manipulations, transformations and data mining. It consolidates all the functions of the entire process into a single workflow. The solution covers all main data wrangling and machine learning techniques, and is based on visual programming.

KNIME Features

KNIME has many valuable key features. Some of the most useful ones include:

Scalability through data handling (intelligent automatic caching of data in the background while maximizing throughput performance)
High extensibility via a well-defined API for plugin extensions
Intuitive user interface
Import/export of workflows
Parallel execution on multi-core systems
Command line version for "headless" batch executions
Activity dashboard
Reporting & statistics
Third-party integrations
Workflow management
Local automation
Metanode linking
Tool blending
Big Data extensions

KNIME Benefits

There are many benefits to implementing KNIME. Some of the biggest advantages the solution offers include:

Integrated Deployment: KNIME’s integrated deployment moves both the selected model, and the entire data model preparation process into production simply and automatically, allowing for continuous optimization in production and also saving time because it eliminates error.

Elastic and Hybrid Execution: KNIME’s elastic and hybrid executions helps you reduce costs while covering periods of high demand, dynamically.

Metadata Mapping: KNIME enables complete metadata mapping of all aspects of your workflow. In addition, KNIME offers blueprint workflows for documenting the nodes, data sources, and libraries used, as well as runtime information.

Guided Analytics: KNIME’s guided analytics applications can be customized based on reusable components.

Powerful analytics, local automation, and workflow difference: KNIME uses advanced predictive and machine learning algorithms to provide you with the analytics you need. In combination with powerful analytics, KNIME’s automation capabilities and workflow difference prepare your organization with the tools you need to make better business decisions.

Supports enterprise-wide data science practices: The deployment and management functionalities of KNIME make it easy to productionize data science applications and services, and deliver usable, reliable, and reproducible insights for the business.

Helps you leverage insights gained from your data: Using KNIME ensures the data science process immediately reflects changing requirements or new insights.

Reviews from Real Users

Below are some reviews and helpful feedback written by PeerSpot users currently using the KNIME solution.

An Emeritus Professor at a university says, “It can read many different file formats. It can very easily tidy up your data, deleting blank rows, and deleting rows where certain columns are missing. It allows you to make lots of changes internally, which you do using JavaScript to put in the conditional. It also has very good fundamental machine learning. It has decision trees, linear regression, and neural nets. It has a lot of text mining facilities as well. It's fairly fully-featured.”

Benedikt S., CEO at SMH - Schwaiger Management Holding GmbH, explains, “All of the features related to the ETL are fantastic. That includes the connectors to other programs, databases, and the meta node function. Technical support has been extremely responsive so far. The solution has a very strong and supportive community that shares information and helps each other troubleshoot. The solution is very stable. The initial setup is pretty simple and straightforward.”

Piotr Ś., Test Engineer at ProData Consult, says, “What I like the most is that it works almost out of the box with Random Forest and other Forest nodes.”

Knime

SAS was founded in 1976 and actually began as a project at North Carolina State University to analyze agriculture research. It has since become a global company that is recognized for its innovation in data analytics and business intelligence. SAS is redefining what's possible with data analytics through greater efficiency, strong information value chains, effective collaboration tools, and state-of-the-art visualization software. SAS Analytics is designed for use in a variety of industries including government, manufacturing, higher education, defense & security, banking, automotive, communications, and much more. SAS Analytics is a business intelligence (BI) solution that has the ability to reveal patterns and anomalies in data, identify relationships and different variables, and predict future outcomes. Users of SAS Analytics will benefit from making more sound, better informed business decisions based on company data and market trends. Data mining, data visualization, text analytics, forecasting, statistical analysis, and more are all available through SAS Analytics. Staples, which boasts $27 billion in sales across the globe, has a business philosophy that prioritizes customer loyalty and satisfaction. In order to better engage their customers, Staples utilizes SAS Analytics to plan finely tuned marketing campaigns. Through forecasting and advanced analytics, Staples has been able to rely on fewer contractors, and cut their marketing budget, while improving their customer retention rate.

SAS

Sample Customers

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

Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG

Aegon, Alberta Parks, Amway China, Axel Springer, Bank of America, Belgium Special Tax, CAP Index, CareSource, CBE Group, Cemig, Center for Responsible Lending, CESCE, Ceska sporitelna, Chantecler, Chico's, Chubb Group of Insurance Companies, CIGNA Thailand, City of Wiesbaden, Germany, Confused.com, Creditreform, Des Moines Area Community College, Deutsche Lufthansa, Directorate of Economics and Statistics, DIRECTV, Dow Chemical Company, Dow Chemical Company, Dun & Bradstreet, EDF Energy, Electrabel GDF SUEZ, ERGO Insurance Group, Erste Bank Croatia, Farmers Mutual Group, Finnair, Florida Department of Corrections, Geneia, Generali Hellas, Genting Malaysia Berhad, Grameenphone, Grandi Salumifici Italiani, HealthPartners, Highmark, Hong Kong Efficiency Unit, HP, Hyundai Securities, Illinois Department of Healthcare and Family ServicesInc Research, ING-DiBa, Institut Pertanian Bogor, InterContinental Hotels Group (IHG), IOM, Kelley Blue Book, Lenovo, Lillebaelt Hospital, Los Angeles County, Maspex Wadowice Group, National Bank of Greece, New Zealand Ministry of Health, New Zealand Ministry of Social Development, Nippon Paper, NMIMS, North Carolina Department of Transportation, North Carolina Office of Information Technology Services, Northern Virginia Electric Cooperative (NOVEC), Oberweis Dairy, ODEC, Ohio Mutual Insurance Group, Oklahoma State University, OneBeacon, Orange Business Services, Orange County Child Support Services, Organic, Orlando Magic, OTP Bank, Plano Independent School District, Project Odyssey, Royal Society for the Protection of Birds, RSA Canada, SCAD, Scotiabank, Singapore National Library Board, Sobeys Inc., SRA International, Staples, Statistics Estonia, Swisscom, SymphonyIRI Group, Telecom Italia, Telef‹nica O2, Town of Cary, Transitions Optical, TrueCar, Turkcell Superonline, UniCredit Bank Serbia, University of Alabama, University of Missouri, USDA National Agricultural Statistics Service

Find out what your peers are saying about Knime, Weka, IBM and others in Data Mining. Updated: May 2025.

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