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 24.7% mindshare in DM, compared to SAS’s 6.8% 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

1,672 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

KNIME

SAS Analytics

Comparison Buyer's Guide

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Executive SummaryUpdated on May 21, 2025

SAS Analytics and KNIME are both established competitors in the data analytics segment. KNIME has an advantage due to its open-source nature, offering flexibility and a better value for its pricing model.

Features: SAS Analytics provides enterprise-ready statistical analysis, advanced forecasting tools, and data visualization, ideal for large enterprises managing complex data. KNIME is noteworthy for its intuitive data blending, machine learning capabilities, and workflow automation, which benefit smaller businesses or those preferring customizable solutions.

Room for Improvement: SAS Analytics could enhance its user interface to be more intuitive and reduce the complexity of some statistical methods. It would also benefit from a streamlined integration process for quicker deployment and a more flexible pricing model. KNIME might improve in offering more direct enterprise support options, providing better documentation for advanced features, and enhancing its dataset handling for exceptionally large data volumes.

Ease of Deployment and Customer Service: SAS Analytics offers extensive deployment options suitable for large-scale setups, though it may require more time to implement. Its well-established support infrastructure provides reliable customer assistance. KNIME offers a straightforward deployment model, being open-source, with an active community-based support system that enables faster integration of solutions.

Pricing and ROI: SAS Analytics has higher initial costs but justifies these with comprehensive features and potential returns for large-scale projects, albeit the investment can be considerable. KNIME's cost-effective model with less immediate expense and favorable ROI makes it particularly appealing to budget-conscious users who prioritize adaptability in analytics solutions.

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

Buyer's Guide

KNIME vs. SAS Analytics

June 2025

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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

KNIME

Ranking in Data Mining

1st

Average Rating

8.2

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

Data Science Platforms (3rd)

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

Mindshare comparison

As of June 2025, in the Data Mining category, 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

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

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

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

"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."

"KNIME is very easy to handle and use. Anyone can use it, and it's easy to learn."

"We can deploy the solution in a cluster as well."

"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."

"I know I don't use it to its full capacity, but I love the Rule Engine feature. It has allowed me to create lookup tables on the fly and break down text fields into quantifiable data."

"It's a coding-less opportunity to use AI. This is the major value for me."

More KNIME pros

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

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

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

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

"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."

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

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

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

More SAS Analytics pros

Cons

"The graphic features of KNIME need improvement"

"To enhance accessibility and user-friendliness, there is a need for improvements in the interface and usability of deep learning and large-scale learning languages."

"For graphics, the interface is a little confusing."

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

"It's difficult to provide input on the improvement area because it's more of self-learning. However, there are times when I am not able to do certain things. I don't know if it's because the solution doesn't allow me or if it's because of the lack of knowledge."

"The visualization functionalities are not good (cannot be compared to, for instance, the possibilities in R)."

"In the last update, KNIME started hiding a lot of the nodes. It doesn't mean hiding, but you need to know what you're looking for. Before that, you had just a tree that you could click, and you could get an overview of what kind of nodes do I have. Right now, it's like you need to know which node you need, and then you can start typing, but it's actually more difficult to find them."

"KNIME doesn't handle large datasets or a high number of records well."

More KNIME cons

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

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

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

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

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

"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."

"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."

More SAS Analytics cons

Pricing and Cost Advice

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

"Scaling to the on-premises version requires a licensing fee per user that is a bit expensive in comparison to R, Python, and SAS."

"I use the tool's free version."

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

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

"There is no cost for using KNIME because it is an open-source solution, but you have to pay if you need a server."

"The price of KNIME is quite reasonable and the designer tool can be used free of charge."

"They have different versions, but I am using the open-source one."

More KNIME 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."

"Setup costs were quite reasonable."

"SAS is very expensive."

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

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

"Prices were comparable with alternative solutions."

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

More SAS Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

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 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 20% of the time

Weka vs SAS Analytics

Compared 16% of the time

Oracle Advanced Analytics vs SAS Analytics

Compared 14% of the time

IBM SPSS Modeler vs SAS Analytics

Compared 13% of the time

More SAS Analytics Competitors

Product Reports

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

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

June 2025

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

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