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KNIME Business Hub vs Weka comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Jul 27, 2025

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 Business Hub
Ranking in Data Mining
1st
Average Rating
8.2
Reviews Sentiment
7.1
Number of Reviews
60
Ranking in other categories
Data Science Platforms (3rd)
Weka
Ranking in Data Mining
4th
Average Rating
7.6
Reviews Sentiment
6.8
Number of Reviews
14
Ranking in other categories
Anomaly Detection Tools (4th)
 

Mindshare comparison

As of October 2025, in the Data Mining category, the mindshare of KNIME Business Hub is 18.7%, down from 26.6% compared to the previous year. The mindshare of Weka is 12.7%, down from 20.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Mining Market Share Distribution
ProductMarket Share (%)
KNIME Business Hub18.7%
Weka12.7%
Other68.6%
Data Mining
 

Featured Reviews

Laurence Moseley - PeerSpot reviewer
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,
AwaisAnwar - PeerSpot reviewer
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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"Stability is excellent. I would give it a nine out of ten."
"There are a lot of connectors available in KNIME."
"This solution is easy to use and it can be used to create any kind of model."
"It can handle an unlimited amount of data, which is the advantage of using Knime."
"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."
"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."
"I was able to apply basic algorithms through just dragging and dropping."
"We have been able to appreciate the considerable reduction in prototyping time."
"It doesn’t cost anything to use the 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."
"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."
"The interface is very good, and the algorithms are the very best."
"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 eliminates the need for coding, allowing you to easily set parameters and complete the majority of the machine learning task with just a few clicks."
 

Cons

"The most difficult part of the solution revolves around its areas concerning machine learning and deep learning."
"​The data visualization part is the area most in need of improvement."
"The overall user experience feels unpolished. In particular: Data field type conversion is a real hassle, and date fields are a hassle; documentation is pretty poor; user community is average at best."
"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."
"The documentation is lacking and it could be better."
"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 solution is inconvenient when it comes to wrangling data that includes multiple steps or features because each step or feature requires its own icon."
"One thing to consider is that the prebuilt nodes may not always be a perfect fit for your specific needs, although most of the time, they work quite well."
"If there are a lot more lines of code, then we should use another language."
"Not particularly user friendly."
"A few people said it became slow after a while."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"Weka could be more stable."
"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."
"The product is good, but I would like it to work with big data. I know it has a Spark integration they could use to do analysis in clusters, but it's not so clear how to use it."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
 

Pricing and Cost Advice

"KNIME desktop is free, which is great for analytics teams. Server is well priced, depending on how much support is required."
"At this time, I am using the free version of Knime."
"We're using the free academic license just locally. I went for KNIME because they have a free academic license."
"It is expensive to procure the license."
"While there are certain limitations in functionality, you can still utilize it efficiently free of charge."
"This is an open-source solution that is free to use."
"KNIME is a cheap product. I currently use KNIME's open-source version."
"This is a free open-source solution."
"The solution is free and open-source."
"We use the free version now. My faculty is very small."
"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."
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Top Industries

By visitors reading reviews
Financial Services Firm
11%
University
9%
Manufacturing Company
9%
Computer Software Company
7%
University
19%
Educational Organization
16%
Computer Software Company
9%
Comms Service Provider
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business20
Midsize Enterprise16
Large Enterprise29
By reviewers
Company SizeCount
Small Business7
Midsize Enterprise1
Large Enterprise2
 

Questions from the Community

What do you like most about KNIME?
Since KNIME is a no-code platform, it is easy to work with.
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.
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...
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?
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...
 

Also Known As

KNIME Analytics Platform
No data available
 

Overview

 

Sample Customers

Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG
Information Not Available
Find out what your peers are saying about KNIME Business Hub vs. Weka and other solutions. Updated: September 2025.
872,019 professionals have used our research since 2012.