SAS Analytics vs Weka comparison

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SAS Analytics

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Executive SummaryUpdated on May 21, 2025

SAS Analytics and Weka are competing in data analysis and machine learning, each offering distinct advantages. SAS Analytics is preferred for its comprehensive analytical features, while Weka stands out for its ease of use and innovation in machine learning.

Features: SAS Analytics offers powerful statistical analysis, predictive analytics, and extensive data management tools. It is valued for its integration capabilities and robust reporting. Weka provides a wide array of machine learning algorithms, an intuitive interface for rapid experimentation, and easy model deployment, differentiating itself with a focus on user-friendliness and innovative solutions.

Room for Improvement: SAS could enhance its user interface to be more beginner-friendly and improve model deployment speed. Additionally, expanding community resources might enrich user engagement. Weka might benefit from better customer support depth, more robust data visualization tools, and enhanced scalability to accommodate larger enterprise-level data analyses.

Ease of Deployment and Customer Service: SAS Analytics ensures smooth deployment processes and comprehensive customer support known for its efficiency in resolving issues. Weka excels in straightforward deployment with accessible user platforms, albeit with community-driven support, which may be less comprehensive compared to SAS’s professional service approach.

Pricing and ROI: SAS Analytics involves higher initial costs justified by its robust toolset and support, promising significant ROI for large-scale enterprises. Weka, as an open-source solution, minimizes initial costs while offering high returns in suitable environments, with ROI depending largely on the users' technical skills. The cost and ROI dynamics distinctly set SAS and Weka apart, with SAS as the premium investment and Weka as a cost-effective alternative.

To learn more, read our detailed SAS Analytics vs. Weka Report (Updated: March 2026).

SAS Analytics vs. Weka

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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

SAS Analytics

Ranking in Data Mining

5th

Average Rating

9.2

Reviews Sentiment

4.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Weka

Ranking in Data Mining

4th

Average Rating

7.8

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

Anomaly Detection Tools (1st)

Mindshare comparison

As of April 2026, in the Data Mining category, the mindshare of SAS Analytics is 7.7%, up from 6.0% compared to the previous year. The mindshare of Weka is 8.8%, down from 21.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining Mindshare Distribution
Product	Mindshare (%)
Weka	8.8%
SAS Analytics	7.7%
Other	83.5%

Data Mining

Featured Reviews

Alejandro Mérida Cárdenas

Senior Manager at Scotiabank

Advanced analytics has expanded risk modeling and simplified multi‑source data extraction

With SAS Analytics, I find that the graphs are very poor, and since I use Power BI, I think SAS Analytics needs to enhance the visualization options to do better visualizations. Many other software have advanced in this area, but I feel SAS Analytics's graphs are not the same. In my experience, many people do not use the graphs of SAS Analytics; instead, they use Power BI or Excel to create graphs because Excel provides more options that SAS Analytics does not. I appreciate that in the last versions, with SAS Viya, there was a significant enhancement in the graphs, but not in SAS Enterprise Guide. The graphic interface of SAS Analytics could be better. The data visualization tools in SAS Analytics should be improved, which is why I use Power BI. I think this is an opportunity area for SAS Analytics. For visualization, I would rate SAS Analytics a nine. I consider SAS Analytics a wonderful tool, but they need to enhance the visualizations.

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reviewer1522338

Phd at a educational organization with 51-200 employees

Using data mining and visualization has improved my research and student learning process

In my opinion, Weka is very useful. I think perhaps a new function with a new algorithm in data mining could be developed. The algorithms currently available in Weka are very good, and all functions are very useful. I have found that I need to use Weka with R and different databases, which has been very useful for me. I am satisfied with Weka's visualization tool. Weka is very easy to use, is very complete, and provides many benefits to the end user. It has many algorithms in data mining and many algorithms to prepare data for the next process in data mining. Weka has many graphical interfaces, though it may not have as many tools for analyzing results.

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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

"It has improved the level of efficacy and validity of our reports, and this has also extended to the area of customer service and relations."

"It's very easy to use once you learn it."

"The team immediately resolves the issues."

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

"This solution has helped us materially grow multiple business categories."

"SAS Analytics is a very versatile and comfortable tool and it is powerful because I can work with data from many millions of records, big data with SAS Analytics in a short time versus other software."

"It has also been around for an extremely long time, has a strong history, and good market penetration."

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

More SAS Analytics pros

"With clustering, if it's a yes, it's a yes, if it's a no, it's a no. It gives you a 100% level of accuracy of a model that has been trained, and that is in most cases, usually misleading. Classification is highly valuable when done as opposed to clustering."

"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."

"Weka is very easy to use, is very complete, and provides many benefits to the end user."

"Weka is a very nice tool, it needs very small requirements. If I want to implement something in Python, I need a lot of memory and space but Weka is very lightweight. Anyone can implement any kind of algorithm, and we can show the results immediately to the client using the one-page feature. The client always wants to know the story. They want the result."

"It is a stable product."

"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."

"I like the machine algorithm for clustering systems. Weka has larger capabilities. There are multiple algorithms that can be used for clustering. It depends upon the user requirements. For clustering, I've used DBSCAN, whereas for supervised learning, I've used AVM and RFT."

"Working with complicated algorithms in huge datasets is really easy in Weka."

More Weka pros

Cons

"Support at universities used to be limited, but I hear this is changing."

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

"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."

"SAS is very expensive. The challenge with them is that you have to pay for a license with every module you buy."

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

"In the graphical area much more improvement is still needed."

"This solution should be made more user-friendly. It needs a better visual output to explain logic to senior executives."

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

More SAS Analytics cons

"I believe is there are a few newer algorithms that are not present in the Weka libraries. Whereas, for example, if I want to have a solution that involves deep learning, so I don't think that Weka has that capability. So in that case I have to use Python for ... predict any algorithms based on deep learning."

"Weka is not horizontally scalable. If I had to run a large dataset over Weka I would have to have a very large usage."

"Not particularly user friendly."

"I'm not sure if it's reliable. It's a little difficult to get results, especially if you are on some other programs like Tableau."

"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."

"Help documentation could be more user friendly."

"In terms of scalability, I think Weka is not prepared to handle a large number of users."

"With Python and R, you can do anything — you have that confidence, but with Weka, I don't have that confidence."

More Weka cons

Pricing and Cost Advice

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

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

"Licensing was rather straightforward."

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

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

"SAS is very expensive."

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

"Prices were comparable with alternative solutions."

More SAS Analytics pricing and cost advice

"We use the free version now. My faculty is very small."

"The solution is free and open-source."

"As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if it is a commercial product."

"Currently, I am using an open-source version so I don't know much about the price of this solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

23%

Computer Software Company

Outsourcing Company

Construction Company

Educational Organization

17%

University

12%

Comms Service Provider

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	10

By reviewers
Company Size	Count
Small Business	8
Midsize Enterprise	1
Large Enterprise	2

Questions from the Community

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 app.

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What needs improvement with SAS Analytics?

SAS Analytics could improve in areas such as handling stress tests and workloads, as well as predicting financial movements, specifically profit and loss movements.

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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 traditional securitization. Recently, I used SAS Analytics ( /products/sas-analytic...

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Product Reports

Data Mining

Download SAS Analytics product report

Download Weka product report

Overview

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

Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.

Sample Customers

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

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SAS Analytics vs. Weka