KNIME vs Weka comparison

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KNIME

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Executive SummaryUpdated on May 21, 2025

KNIME and Weka are both key players in the data analytics and data mining fields. KNIME appears to have a competitive edge due to its strong integration with various technologies and its flexibility which appeals to data scientists and analysts.

Features: KNIME offers a comprehensive suite of tools for data analytics, with robust ETL process support and technology integration with languages like R, Python, and Java. It allows application of complex algorithms without needing to write code. Weka is known for its diverse algorithm options that are effective for rapid testing and allows easy integration of new algorithms, offering flexibility for those with a Java background.

Room for Improvement: KNIME could enhance its data visualization capabilities and improve handling of large datasets. The documentation could also be more comprehensive and user-friendly. Weka needs to improve its scalability and performance due to limitations with the Java Virtual Machine. There are also noted issues with its documentation and pre-processing tools which could be more intuitive.

Ease of Deployment and Customer Service: KNIME can be deployed in both on-premises and cloud environments, supporting flexibility, and is supplemented by extensive community support despite critiques of its documentation. Weka supports primarily on-premises deployments, lacks extensive customer service resources, and relies heavily on community forums, which might challenge users needing professional support.

Pricing and ROI: KNIME is an open-source platform offering a free desktop version suitable for smaller teams or individual users, with a server version available for enterprise needs. It is considered cost-effective given its feature set compared to paid solutions. Weka is also open-source, providing an attractive option for cost-effectiveness, though additional investment in complementary tools may be needed for full project requirement fulfillment.

To learn more, read our detailed KNIME vs. Weka Report (Updated: July 2025).

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KNIME vs. Weka

July 2025

Helped 861,524 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)

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 KNIME is 24.6%, down from 27.0% 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

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,

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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

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

"KNIME is easy to learn."

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

"It's a huge tool with machine learning features as well."

"Clear view of the data at every step of ETL process enables changing the flow as needed."

"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 am impressed by the modularity and reusability in KNIME, especially the ability to make small adjustments to object configurations."

"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."

More KNIME pros

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

"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."

"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."

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

"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 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."

"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."

More Weka pros

Cons

"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."

"There should be better documentation and the steps should be easier."

"Both RapidMiner and KNIME should be made easier to use in the field of deep learning."

"Sometimes, we needed more space to handle larger operations, especially since our machines had limited space and memory due to Kubernetes clusters."

"They could add more detailed examples of the functionality of every node, how it works and how we can use it, to make things easier at the beginning."

"They should look at other vendors like Alteryx that are more user friendly and modern."

"KNIME is not scalable."

"I'd like something that would make it easier to connect/parse websites, although I will fully admit that I'm not as proficient in KNIME as I would like to be, so it could be I'm just missing something."

More KNIME cons

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

"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 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."

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

"If you have one missing value in your dataset and this missing value belongs to a specific attribute and the attribute is a numeric attribute and there is only one missing data, whenever you import this data, the problem is that Weka cannot understand that this is a numeric field. It converts everything into a string, and there is no way to convert the string into numerical math. It's really very complicated."

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

"Weka could be more stable."

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

More Weka cons

Pricing and Cost Advice

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

"This is an open-source solution that is free to use."

"For beginners, the free desktop version is very attractive, but the full server version can be more expensive. I have only used the free version and it offers a fair pricing system. I have been promoting it to others without any compensation or request from the company, simply because I am enthusiastic about it. I am not aware of the pricing for the server version, but it seems to be widely used."

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

"With KNIME, you can use the desktop version free of charge as much as you like. I've yet to hit its limits. If I did, I'd have to go to the server version, and for that you have to pay. Fortunately, I don't have to at the moment."

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

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

More KNIME pricing and cost advice

"The solution is free and open-source."

"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."

"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

12%

Manufacturing Company

10%

Computer Software Company

University

18%

Educational Organization

17%

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 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 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?

Altair RapidMiner vs KNIME

Comparisons

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Compared 14% of the time

Microsoft Power BI vs KNIME

Compared 14% of the time

Alteryx vs KNIME

Compared 8% of the time

Databricks vs KNIME

Compared 5% of the time

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IBM SPSS Statistics vs Weka

Compared 15% of the time

IBM SPSS Modeler vs Weka

Compared 9% of the time

SAS Analytics vs Weka

Compared 5% of the time

Splunk User Behavior Analytics vs Weka

Compared 5% of the time

Oracle Advanced Analytics vs Weka

Compared 4% of the time

More Weka Competitors

Product Reports

Download KNIME product report

KNIME

July 2025

Download Weka product report

June 2025

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

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

Infocom Corporation, Dymatrix Consulting Group, Soluzione Informatiche, MMI Agency, Estanislao Training and Solutions, Vialis AG

Information Not Available