KNIME Business Hub vs SAP Predictive Analytics [EOL] comparison

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Knime and SAP are both solutions in the Data Science Platforms category. Additionally, 94% of Knime users are willing to recommend the solution, compared to 100% of SAP users who would recommend it.

KNIME Business Hub

Read 66 KNIME Business Hub reviews

9,626 Views
7,316 Comparison Views

94% willing to recommend

SAP Predictive Analytics [EOL]

Read 3 SAP Predictive Analytics [EOL] reviews

100% willing to recommend

KNIME Business Hub

SAP Predictive Analytics [EOL]

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Executive SummaryUpdated on Jun 3, 2026

KNIME Business Hub and SAP Predictive Analytics [EOL] compete in the data analytics sector. KNIME Business Hub appears to have an advantage due to its support and cost-effectiveness, but SAP Predictive Analytics [EOL] offers more sophisticated features, which is a significant value add.

Features: KNIME Business Hub offers collaborative capabilities and easy integration with multiple data sources. It includes an extensive library of machine learning algorithms. Alternatively, SAP Predictive Analytics [EOL]'s strength lies in predictive modeling with advanced automation.

Ease of Deployment and Customer Service: KNIME Business Hub uses a straightforward cloud deployment model, combined with strong, responsive customer support. SAP Predictive Analytics [EOL] involves a complex on-premise setup but offers extensive customization options.

Pricing and ROI: KNIME Business Hub maintains a competitive pricing structure with a promising ROI due to low initial costs. On the other hand, SAP Predictive Analytics [EOL] requires more substantial upfront investment, yet provides a longer-term return on investment due to its advanced predictive analytics.

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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 Business Hub

Average Rating

8.2

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

Data Mining (1st), Data Science Platforms (3rd)

SAP Predictive Analytics [EOL]

Average Rating

8.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Featured Reviews

NataliaRaffo

Co Founder & Chief Data Officer Cdo at NTT DATA

Workflow automation has accelerated advanced analytics and machine learning delivery

Sometimes it is a little bit difficult to use some nodes when we have many large-scale data, for example, CSV files with a large amount of data. It is sometimes difficult to try to import the data in KNIME Business Hub nodes because I think that some features that are in the CSV in text, for example, large text, is difficult for KNIME Business Hub to import these fields. I don't know why, but it is very difficult. We need to try to use different nodes for importing the data, such as File Reader and CSV Reader. However, I think that it is always the features that have much text, it is difficult for KNIME Business Hub to understand and import this information. I don't know why, or maybe I don't know if we don't know what the better option is to configure the node to import all the CSV or the data set. However, we have always had this problem. In some nodes, sometimes it is the same because sometimes, for example, I have a CSV and in my CSV, I have a feature that is, for example, a date. When I import this data set in the File Reader node, I have problems with this field because it is a date, but the problem is that it imports it as text, for example. We try to use their nodes that convert text to date, but sometimes it is difficult, and it is not immediate to transform the text into a date. So we needed to convert the text into a date in the CSV, and then import it again in the KNIME Business Hub node and try to have a good read of this field. I know that KNIME Business Hub has some nodes to convert text to date and others, but sometimes it is difficult to use these nodes. I don't know why. Maybe it needs a specific format for the date and we need to transform our feature in this option. So sometimes it is a large process to convert these features. However, sometimes we need to investigate and search for other nodes, and try with other nodes to import these cases.

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Gary Cook

Executive at Empowered Analytics

Enables us to forecast and pull trends and has an easy installation

My rating for SAP Predictive Analytics would be an eight out of ten. If I have to be bold, I'll probably say that we're building away hours, and we are actually putting a lot of the actual predicting stuff back into the warehouse. So running it very bi-directionally. So I'm not sure what its integration features are at the moment, but that's an area we're going to look into in the next month or so.

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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

"KNIME has improved our organization because we are able to collect data in a way that we can interpret and it provides visuals."

"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."

"The technical support is excellent, fast, accurate, and friendly."

"From a user-friendliness perspective, it's a great tool."

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

"For me, KNIME Business Hub changed the way I work with data, because instead of doing repetitive manual tasks, I can focus on the analysis and the results."

"We have found KNIME valuable when it comes to its visualization."

"KNIME is easy to learn."

More KNIME Business Hub pros

"I think the features of the actual ability to forecast and pull trends and correlations has been really good."

"I have found that the solution is very stable."

"The most valuable features are the analytics and reporting."

"We always purchase SAP support because it is very good."

"SAP Predictive Analytics is better suited for business users because it hides the complexity of the model, whereas Microsoft Azure Machine Learning provides a lot more flexibility for technical professionals to tweak the model."

Cons

"The diversity of native algorithms could be improved."

"The user interface could be a bit better."

"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."

"We are worried about the performance when it comes to using a lot of data that has many rows and columns."

"I think some of the online training content could be better, although I have been able to find all of the information."

"KNIME needs to provide more documentation and training materials, including webinars or online seminars."

"It's pretty straightforward to understand. So, if you understand what the pipeline is, you can use the drag-and-drop functionality without much training. Doing the same thing in Python requires so much more training. That's why I use KNIME."

"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."

More KNIME Business Hub cons

"This solution works for acquired data but not live, real-time data."

"The license fee appears to be prohibitively expensive and overly secretive, leading our clients to opt for cloud-based solutions that only charge for data storage and processing time."

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."

"I use the open-source version."

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

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

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

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

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

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

More KNIME Business Hub pricing and cost advice

"The pricing is reasonable"

"A free trial version is available for testing out this solution."

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Top Industries

By visitors reading reviews

Financial Services Firm

11%

Manufacturing Company

University

Outsourcing Company

18%

Construction Company

15%

Transportation Company

Manufacturing Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	22
Midsize Enterprise	16
Large Enterprise	35

No data available

Questions from the Community

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?

KNIME Business Hub could be improved by making the user experience more intuitive, especially for users who are new to data analytics. More guided features and documentation would be beneficial.

See all answers

What is your primary use case for KNIME?

I automated the processing of paper-based data by digitalizing scanned documents and creating a KNIME Business Hub workflow that transforms the data into an analysis-ready format, reducing a manual...

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Product Reports

Buyer's Guide

KNIME Business Hub

September 2026

Download KNIME Business Hub product report

Buyer's Guide

Data Science Platforms

September 2026

Download SAP Predictive Analytics [EOL] product report

Also Known As

KNIME Analytics Platform

SAP BusinessObjects Predictive Analytics, BusinessObjects Predictive Analytics, BOPA

Overview

KNIME Business Hub offers a no-code interface for data preparation and integration, making analytics and machine learning accessible. Its extensive node library allows seamless workflow execution across various data tasks.

KNIME Business Hub stands out for its user-friendly, no-code platform, promoting efficient data preparation and integration, even with Python and R. Its node library covers extensive data processes from ETL to machine learning. Community support aids users, enhancing productivity with minimal coding. However, its visualization, documentation, and interface require refinement. Larger data tasks face performance hurdles, demanding enhanced cloud connectivity and library expansions for deep learning efficiencies.

What are the most important features of KNIME Business Hub?

No-Code Interface: Simplifies data preparation without coding expertise.
Node Library: Supports diverse data tasks, including ETL and machine learning.
Python and R Integration: Seamlessly connects with popular programming languages.
Automation Capabilities: Enhances workflow productivity.
Visual Workflow Tools: Offers efficient data handling with visual aids.

What benefits and ROI can users expect from KNIME Business Hub?

Increased Productivity: Streamlined data tasks reduce development time.
Community Support: Access to extensive resources and user assistance.
Cost-Effective: Robust performance even on standard hardware.
Accessible Learning Curve: Simplicity allows easy access for educational and professional use.
Enhanced Prototyping: Ideal for decision model testing.

KNIME Business Hub finds application in data transformation, cleansing, and multi-source integration for analytics and reporting. Companies utilize it for predictive modeling, clustering, classification, machine learning, and automating workflows. Its coding-free approach suits educational and professional settings, assisting industries in data wrangling, ETLs, and prototyping decision models.

Knime

SAP Predictive Analytics [EOL] offered a powerful platform for creating predictive models that supported business decision-making by utilizing historical data to anticipate future trends.

SAP Predictive Analytics [EOL] was designed to integrate with existing SAP environments, allowing businesses to leverage their existing data infrastructure. It provided users with intuitive tools to automate data preparation and model management, simplifying complex analytical processes. Data scientists could efficiently build and deploy predictive models to address specific business questions. SAP emphasized ease of deployment and scalability, ensuring the platform met the needs of data-driven enterprises.

What are the key features?

Automated Model Creation: Streamlines the process of generating predictive models, reducing time and effort.
Data Preparation: Simplifies data cleaning and preparation, ensuring high-quality input for analysis.
Seamless Integration: Easily integrates with SAP environments, enhancing data accessibility.

What benefits should users consider in reviews?

Informed Decision-Making: Provides insights that help businesses make data-informed decisions.
Efficiency in Analysis: Reduces the time needed to develop and deploy predictive models.
Scalability: Supports expanding data needs as businesses grow.

In industries like manufacturing and retail, SAP Predictive Analytics [EOL] helped optimize supply chains and inventory management by forecasting demand trends. Financial sector users implemented it to enhance risk analysis and fraud detection models, providing valuable insights for mitigating potential risks.

SAP

Sample Customers

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

mBank

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September 2026

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