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: April 2026).

SAS Analytics vs. Weka

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Helped 892,678 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:

Categories and Ranking

SAS Analytics

Ranking in Data Mining

5th

Average Rating

9.2

Reviews Sentiment

5.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Weka

Ranking in Data Mining

4th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Anomaly Detection Tools (1st)

Mindshare comparison

As of May 2026, in the Data Mining category, the mindshare of SAS Analytics is 8.1%, up from 6.6% compared to the previous year. The mindshare of Weka is 7.3%, down from 18.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining Mindshare Distribution
Product	Mindshare (%)
Weka	7.3%
SAS Analytics	8.1%
Other	84.6%

Data Mining

Featured Reviews

Sanjay Charitesh Makam

Student at Rochester Institute of Technology

Data management has become faster and clearer but integration and large data handling still need work

SAS Analytics needs to improve for entry-level people in my college because buying licenses is somewhat costly for small businesses. SAS Analytics is also somewhat complex for students who are entering the data field. Additionally, SAS Analytics is not good at integration with other environments, so it should improve on that, and some syntaxes are not good. SAS Analytics has an older architecture in some areas, so it should enhance the UX side and performance in some aspects. SAS Analytics documentation should improve to be clearer for students who are currently using or trying to use SAS Analytics, so the documentation should be simplified. Customer service is pretty good as of now, as they respond in two to four hours on business days, but faster response times may solve many problems and attract more users. I rate SAS Analytics seven or eight out of ten. SAS Analytics should improve in massive data handling, as I mentioned earlier. SAS Analytics struggles when it handles massive data, and it should also get top-tier support. The support should improve from two to four hours down to one hour or even thirty minutes. SAS Analytics should also offer more low-code options for those who do not know how to code or to save more time, and they should consider reducing their pricing. Currently, SAS Analytics has limited flexibility and very limited integration, so improvements in those areas are necessary. SAS Analytics student version does not handle long or lengthy data well right now, so it should improve on that point. As I have mentioned, it struggles with larger data sets, but for current small or medium data sets, it is very good. Customer support should improve to fifteen to thirty minutes or immediate live agents during business hours. The two to four hours wait can sometimes take more than one day, and that does not make sense. We have to stop all our work until they reply if we encounter critical problems, so they should improve for certain.

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Sanjay Charitesh Makam

Student at Rochester Institute of Technology

Data mining projects have become faster and visualization now guides our pattern discovery

Weka can be improved in some areas. As a one-year user, I can share my experiences. I have tried installing other packages into Weka using the inbuilt package manager or adding modern algorithms that I wish to apply to my data sets or data processing. I think Weka needs to improve in that and also in integrating Python into Weka, which would help users much more. I chose eight out of ten because we need improvements in Weka, such as installing inbuilt packages. Weka uses skips such as N+2 or N+4 and eight-node, which makes sense basically, but I think it should improve in the package attribute, such as configuring Python easily and adding modern algorithms into Weka more easily.

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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 facilitated timely analysis results with quality work and meaningful output."

"All of the data analytics features in SAS Analytics are valuable to us since we're using them daily across our entire analytics team."

"The most valuable aspect of SAS Analytics is the ability to automate work using macros without heavily relying on technology teams, allowing me to create functions that operate independently and streamline workflows efficiently."

"It has facilitated timely analysis results with quality work, meaningful output, and consistent data use."

"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."

"I think that the cost-benefit ratio is okay, it is the right balance."

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

More SAS Analytics pros

"I think Weka is definitely a good investment, that is why we still use it."

"The path of machine learning in classification and clustering is useful. The GUI can get you results. No programming is needed. No need to write down your script first or send to your model or input your data."

"Using Weka has positively impacted my studies and projects because it has saved my time firstly."

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

"Weka is a very easy to use Data Mining solution, great for learning and for doing small experiments before exploring the data deeper, with a large number and diversity of algorithms that make it an excellent solution for rapid testing."

"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."

More Weka pros

Cons

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

"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."

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

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

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

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

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

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

More SAS Analytics cons

"The solution doesn’t really have very good technical support."

"Scalability and performance are the main aspect of improvement in Weka, since it has the main Java limitations, regarding the JVM."

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

"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."

"Help documentation could be more 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."

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

More Weka cons

Pricing and Cost Advice

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

"SAS is very expensive."

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

"Setup costs were quite reasonable."

"Licensing was rather straightforward."

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

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

"Prices were comparable with alternative solutions."

More SAS Analytics pricing and cost advice

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

"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."

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

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Top Industries

By visitors reading reviews

Financial Services Firm

25%

University

15%

Construction Company

Outsourcing Company

Educational Organization

19%

University

14%

Comms Service Provider

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	2
Large Enterprise	11

By reviewers
Company Size	Count
Small Business	11
Midsize Enterprise	1
Large Enterprise	3

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?

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...

What is your primary use case for SAS Analytics?

I work with SAS Analytics solutions, specifically with SAS Data Mining, because I extract information from the core system of the bank via ODBC connections and use SAS Base on the servers. I am the...

What is your experience regarding pricing and costs for Weka?

My experience with pricing, setup cost, and licensing for Weka is that I think it's a fair price since we are using it academically, so it is completely free to download and use. The license is als...

What needs improvement with Weka?

What is your primary use case for Weka?

My main use case for Weka is data exploration and data processing and data mining. I can give a quick, specific example of how I've used Weka for data exploration or data processing: we have a subj...

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Product Reports

SAS Analytics

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Download Weka product report

Overview

SAS Analytics offers a powerful suite of tools for statistical analysis, predictive analytics, and data handling, making it ideal for industries requiring robust data-driven decisions. Its extensive capabilities cater to professionals familiar with SQL and demand forecasting needs across sectors.

With a strong presence in analytics, SAS Analytics provides a seamless experience for data preparation, exploration, and reporting. Users benefit from its ability to handle large data sets, generate interactive reports, and integrate with multiple platforms. Despite its high costs and need for improved visualization and natural language querying, SAS Analytics remains a favored choice for those requiring comprehensive statistical modeling and risk analytics. Enhancing self-service analytics and accelerating support response times are areas of needed improvement. Companies use it extensively for business intelligence and demand forecasting, particularly in sectors like banking and financial services.

What are the key features of SAS Analytics?

Interactive Reports: Embeds intuitive and engaging reports.
Extensive Analytics Functions: Offers a wide range of analytics capabilities.
SQL Compatibility: Streamlines use for SQL-proficient users.
Predictive Analytics: Enhances analysis with SAS E-Miner.
Automation: Utilizes macros for automated tasks.
Large Data Handling: Manages extensive data effectively.

What benefits can users look for in reviews?

Increased Productivity: Saves time with automation and efficient data processing.
Enhanced Efficiency: Direct data source connections streamline workflows.
Versatility: Adapts to diverse analytical and reporting requirements.
Comprehensive Resources: Provides extensive help and support materials.

SAS Analytics is widely implemented in industries for tasks like national auto insurance pricing, financial replication, and marketing analytics. Teams in banking and financial services apply it for quantitative analyses, risk assessments, and generating detailed operational reports, demonstrating its adaptability and strength in handling complex data scenarios.

SAS

Weka provides a user-friendly platform for data processing and classification with a no-code interface, visual tools, and diverse algorithms. Its robust GUI supports seamless enterprise data integration and efficient performance on large datasets.

Weka is known for its simplicity and comprehensive algorithm selection, making it a popular choice for data exploration, processing, clustering, and mining. The platform is user-friendly and caters to both beginners and advanced users, supporting machine learning algorithms like classification, J48, KNN, regression, and clustering. Users leverage Weka for anomaly detection, data cleansing, and visualization, often in research and educational settings. Despite its strengths, users seek better Python integration and enhanced deep learning support, as well as improvements in data visualization, installation, and scalable solutions for big data scenarios.

What key features does Weka offer?

Simplicity: Intuitive design with a no-code interface for rapid project execution
Visualization: Comprehensive tools to assist in data interpretation and decision-making
Algorithm Variety: Wide range of options for data processing and classification
Performance: Efficient handling of large datasets with seamless enterprise integration
User-Friendliness: Robust GUI that assists both beginners and experienced users

What benefits and ROI do users see?

Time-Saving: Quick deployment with intuitive interfaces reduces project timelines
Flexibility: Adaptable to both on-premise and cloud-based deployment environments
Comprehensive Mining: Extensive capabilities allow for exhaustive data exploration
Ease of Use: Design caters to a range of users, enhancing accessibility and productivity

Weka is used across industries for projects involving data exploration and machine learning, enhancing research and educational initiatives. It transforms decision trees and neural networks, catering to diverse deployment requirements.

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

Information Not Available

SAS Analytics vs. Weka