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: June 2025).

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

6.5

Number of Reviews

Ranking in other categories

No ranking in other categories

Weka

Ranking in Data Mining

2nd

Average Rating

7.6

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Anomaly Detection Tools (4th)

Mindshare comparison

As of July 2025, in the Data Mining category, the mindshare of SAS Analytics is 7.3%, down from 7.8% compared to the previous year. The mindshare of Weka is 17.7%, down from 21.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining

Featured Reviews

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.

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AwaisAnwar

Treasury Management in Finance Department at National University of Pakistan

Open source, good for basic data mining use cases except for the visualization results

I haven't found it particularly useful. It lacks state-of-the-art algorithms and impressive outcomes. While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results. Moreover, a new user interface would be great, especially for beginners. Something that guides them through the available tools and helps them achieve their goals. I haven't seen anything like that myself, though maybe it's there and I missed it.

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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

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

"The most valuable feature is the ability to handle large data sets."

"It is able to connect to all major platforms, and all the smaller platforms that I have come across."

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

"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."

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

"The technical support is okay."

More SAS Analytics pros

"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."

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

"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."

"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."

"There are many options where you can fill all of the data pre-processing options that you can implement when you're importing the data. You can also normalize the data and standardize it in an easier way."

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

"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."

"It doesn’t cost anything to use the product."

More Weka pros

Cons

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

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

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

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

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

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

"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."

"This solution should be made more user-friendly."

More SAS Analytics cons

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

"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."

"If there are a lot more lines of code, then we should use another language."

"The filter section lacks some specific transformation tools. If you want to change a variable from a numeric variable to a categorical variable, you don't have a feature that can enable you to change a variable from a numeric variable to a categorical variable."

"A few people said it became slow after a while."

"The visualization of Weka is subpar and could improve. Machine learning and visualization do not work well together. For example, we want to know how we can we delete empty cells or how can we fill in the empty cells without cleaning the data system and putting it together."

"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."

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

More Weka cons

Pricing and Cost Advice

"SAS is very expensive."

"Setup costs were quite reasonable."

"Prices were comparable with alternative solutions."

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

"Licensing was rather straightforward."

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

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

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

More SAS Analytics pricing and cost advice

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

"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."

"The solution is free and open-source."

"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

20%

Computer Software Company

10%

Insurance Company

Educational Organization

University

17%

Educational Organization

16%

Computer Software Company

11%

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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.

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.

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...

What do you like most about Weka?

It is a stable product.

What is your experience regarding pricing and costs for Weka?

Weka is free and open-source software. That is why I used it over KNIME.

What needs improvement with Weka?

IBM SPSS Statistics vs SAS Analytics

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Compared 18% of the time

IBM SPSS Modeler vs SAS Analytics

Compared 15% of the time

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KNIME vs Weka

Compared 62% of the time

IBM SPSS Statistics vs Weka

Compared 15% of the time

IBM SPSS Modeler vs Weka

Compared 9% of the time

Splunk User Behavior Analytics vs Weka

Compared 5% of the time

Oracle Advanced Analytics vs Weka

Compared 4% of the time

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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