KNIME vs SAS Analytics comparison

Knime and SAS are both solutions in the Data Mining category. Knime is ranked #1 with an average rating of 7.9, while SAS is ranked #5 with an average rating of 9.0. Knime holds a 25.2% mindshare in DM, compared to SAS’s 6.6% mindshare. Additionally, 94% of Knime users are willing to recommend the solution, compared to 94% of SAS users who would recommend it.

KNIME

Read 60 KNIME reviews

1,766 Views
1,278 Comparison Views

94% willing to recommend

SAS Analytics

Read 11 SAS Analytics reviews

574 Views
374 Comparison Views

94% willing to recommend

KNIME

SAS Analytics

Comparison Buyer's Guide

Download the report

Executive Summary

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

Find out in this report how the two Data Mining solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.

To learn more, read our detailed KNIME vs. SAS Analytics Report (Updated: April 2025).

Buyer's Guide

KNIME vs. SAS Analytics

April 2025

Download the complete report

Helped 851,604 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

KNIME

Ranking in Data Mining

1st

Average Rating

8.2

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (2nd)

SAS Analytics

Ranking in Data Mining

5th

Average Rating

9.2

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of May 2025, in the Data Mining category, the mindshare of KNIME is 25.2%, down from 27.5% compared to the previous year. The mindshare of SAS Analytics is 6.6%, down from 8.9% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Mining

Featured Reviews

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,

Read full review

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

"The ETL which helps me to collect, reformat, and load the data from multiple sources into one destination, a storage database."

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

"The most useful features are the readily available extensions that speed up the work."

"This solution is easy to use and especially good at data preparation and wrapping."

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

"I would rate the stability of KNIME a ten out of ten."

"The most valuable features of KNIME are its ability to convert your sub-workflow into a node. For example, the workflow has many individual native nodes that can be converted into a single node. This representation has simplified my workflow to a great extent. I can present my workflow in a very compact way."

"Usability, and organising workflows in very neat manner. Controlling workflow through variables is something amazing."

More KNIME pros

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

"The team immediately resolves the issues."

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

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

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

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

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

More SAS Analytics pros

Cons

"KNIME could improve when it comes to large data markets."

"It could be easier to use."

"There are some parameters that I would like to have at a bigger scale. The upper limit of one node that tries to find spots or areas in photos was too small for us. It would need to be bigger."

"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."

"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 is not good at visualization."

"In my environment, I need to access a lot of servers with different characteristics and access methods. Some of my servers have to be accessed using proxy which is not supported by KNIME, so I still need to create the middleware to supply the source of my KNIME configurations."

"The documentation is lacking and it could be better."

More KNIME cons

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

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

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

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

"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."

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

"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."

More SAS Analytics cons

Pricing and Cost Advice

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

"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."

"I use the tool's free version."

"KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required."

"While there are certain limitations in functionality, you can still utilize it efficiently free of charge."

"The client versions are mostly free, and we pay only for the KNIME server version. It's not a cheap solution."

"At this time, I am using the free version of Knime."

"There is a Community Edition and paid versions available."

More KNIME pricing and cost advice

"Setup costs were quite reasonable."

"Prices were comparable with alternative solutions."

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

"Licensing was rather straightforward."

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

"SAS is very expensive."

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

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

More SAS Analytics pricing and cost advice

See which vendors are best for you

Use our free recommendation engine to learn which Data Mining solutions are best for your needs.

See recommendations

851,604 professionals have used our research since 2012.

Top Industries

By visitors reading reviews

Financial Services Firm

12%

Manufacturing Company

11%

Computer Software Company

University

Financial Services Firm

19%

Computer Software Company

10%

Educational Organization

Insurance Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

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 version, 5.4, when it becomes available. The machine learning and profileration asp...

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 app.

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 movements, specifically profit and loss movements.

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

See all answers

Comparisons

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 18% of the time

Weka vs SAS Analytics

Compared 16% of the time

Oracle Advanced Analytics vs SAS Analytics

Compared 13% of the time

IBM SPSS Modeler vs SAS Analytics

Compared 13% of the time

More SAS Analytics Competitors

Product Reports

Buyer's Guide

KNIME

May 2025

Download KNIME product report

Buyer's Guide

Data Mining

May 2025

Download SAS Analytics product report

Also Known As

KNIME Analytics Platform

No data available

Overview

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

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

Buyer's Guide

KNIME vs. SAS Analytics

April 2025

Free Report: KNIME vs. SAS Analytics

Find out what your peers are saying about KNIME vs. SAS Analytics and other solutions. Updated: April 2025.

DOWNLOAD NOW

851,604 professionals have used our research since 2012.

See our KNIME vs. SAS Analytics report.

See our list of best Data Mining vendors.

We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.