Darwin vs SAP Predictive Analytics [EOL] comparison

SparkCognition and SAP are both solutions in the Data Science Platforms category. SparkCognition is ranked #25, while SAP is ranked #27. SparkCognition holds a 1.6% mindshare in Data Science Platforms, compared to SAP’s 1.4% mindshare. Additionally, 100% of SparkCognition users are willing to recommend the solution, compared to 100% of SAP users who would recommend it.

Darwin

Read 8 Darwin reviews

1,001 Views
991 Comparison Views

100% willing to recommend

SAP Predictive Analytics [EOL]

Read 3 SAP Predictive Analytics [EOL] reviews

783 Views
775 Comparison Views

100% willing to recommend

Darwin

SAP Predictive Analytics [EOL]

Comparison Buyer's Guide

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Executive SummaryUpdated on Apr 15, 2026

SAP Predictive Analytics [EOL] and Darwin compete in the predictive analytics category. Darwin appears superior due to its advanced algorithms and data processing capabilities.

Features: SAP Predictive Analytics [EOL] provides robust integration with SAP systems and automates model building. Darwin offers advanced machine learning with automated feature engineering and a user-friendly interface.

Ease of Deployment and Customer Service: SAP Predictive Analytics [EOL] integrates seamlessly in SAP setups but can be challenging elsewhere. Darwin provides flexibility with cloud-based deployment and responsive support.

Pricing and ROI: SAP Predictive Analytics [EOL] has higher setup costs due to integration needs but offers strong returns for SAP-centric users. Darwin presents a cost-effective setup with scalable pricing, ensuring strong ROI across various contexts.

To learn more, read our detailed Data Science Platforms Report (Updated: April 2026).

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April 2026

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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

Darwin

Ranking in Data Science Platforms

25th

Average Rating

8.0

Reviews Sentiment

6.7

Number of Reviews

Ranking in other categories

No ranking in other categories

SAP Predictive Analytics [EOL]

Ranking in Data Science Platforms

27th

Average Rating

8.6

Reviews Sentiment

7.1

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2026, in the Data Science Platforms category, the mindshare of Darwin is 1.6%, up from 0.3% compared to the previous year. The mindshare of SAP Predictive Analytics [EOL] is 1.4%, up from 0.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms Mindshare Distribution
Product	Mindshare (%)
Darwin	1.6%
SAP Predictive Analytics	1.4%
Other	97.0%

Data Science Platforms

Featured Reviews

AaronCooke

Founder at Helio Summit

Empowers SMEs to build solutions and interface them with the existing business systems, products and workflows.

There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do. Because it's so much better than traditional methods, we don't get a ton of complaints of, "Oh, we wish we could do that." Most people are happy to see that they can build models that quickly, and that it can be done by the people who actually understand the problem, i.e. SMEs, rather than having to rely on data scientists. There's a small learning curve, but it's shorter for an SME in a given industry to learn Darwin than it takes for data scientists to learn industry-specific problems. The industry I work in deals with tons and tons of data and a lot of it lends itself to Darwin-created solutions. Initially, there were some limitations around the size of the datasets, the number of rows and number of columns. That was probably the biggest challenge. But we've seen the Darwin product, over time, slowly remove those limitations. We're happy with the progress they've made.

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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

"Darwin is the perfect tool to solve this issue; what the machine-learning industry needs at this point to expand exponentially in the oil and gas market."

"I liked the data checking feature where it looks at your data and sees how viable it is for use. That's a really cool feature. Automatic assessment of the quality of datasets, to me, seems very valuable."

"Our main goal is to transform data into knowledge and Darwin is definitely helping us to do that faster."

"Even people who are not fully technical can use it with a little guidance or something."

"The thing that I find most valuable is the ability to clean the data."

"We have managed to reduce the percentage of high-risk clients from 8.9 percent to 5 percent, which is very significant in the overall quality of our clients and very good news for everyone, especially our investors."

"Darwin is really useful for people who don't necessarily do a lot of data science."

"When we have a clean dataset, within two to three hours we have a really nice model, one that is better than we could generate in a week."

More Darwin pros

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

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

"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."

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

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

Cons

"There's always room for improvement in the UI and continuing to evolve it to do everything that the rest of AI can do."

"The license cost is not cheap, especially not for markets like Mexico."

"In the beginning, when we started to see how Darwin works, we thought that maybe, from raw, dirty data, we could generate a model really fast, but that's not true."

"There are issues around the ethics of artificial intelligence and machine learning. You need to have a lot of transparency regarding what is going on under the hood in order to trust it. Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets."

"Our main data repository is on AWS. The trouble we are having is that we have to download the data from our repository to bring it into Darwin. It would be great if there was an API to connect our repository to Darwin."

"The analyze function takes a lot of time."

"We have used Darwin as a complement to other tools like R and SPSS to get the accuracy we want."

"Something they are working on, which is great, is to have an API that can access data directly from the source. Currently, we have to create a specific dataset for each model."

More Darwin 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."

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

Pricing and Cost Advice

"As far as I understand, my company is not paying anything to use the product."

"In just six months, we calculated six million pesos that we have prevented in revenue from going away with another customer because of this solution. Thanks to Darwin, we didn't lose those six million pesos."

"The license cost is not cheap, especially not for markets like Mexico. But sometimes, you do have to make these leap of faith for some tools to see if they can get you the disruption that you are aiming for. The investment has paid off for us very well."

"I believe our cost is $1,000 per month."

"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

16%

Manufacturing Company

11%

Construction Company

11%

University

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	6
Large Enterprise	2

No data available

Comparisons

Databricks vs Darwin

Compared 9% of the time

KNIME Business Hub vs Darwin

Compared 6% of the time

Altair RapidMiner vs Darwin

Compared 6% of the time

Amazon Comprehend vs Darwin

Compared 6% of the time

Dataiku vs Darwin

Compared 6% of the time

More Darwin Competitors

KNIME Business Hub vs SAP Predictive Analytics [EOL]

Compared 8% of the time

Saturn Cloud vs SAP Predictive Analytics [EOL]

Compared 7% of the time

IBM SPSS Statistics vs SAP Predictive Analytics [EOL]

Compared 7% of the time

Starburst Galaxy vs SAP Predictive Analytics [EOL]

Compared 6% of the time

Altair RapidMiner vs SAP Predictive Analytics [EOL]

Compared 6% of the time

More SAP Predictive Analytics [EOL] Competitors

Product Reports

Buyer's Guide

Darwin

April 2026

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Buyer's Guide

Data Science Platforms

April 2026

Download SAP Predictive Analytics [EOL] product report

Also Known As

No data available

SAP BusinessObjects Predictive Analytics, BusinessObjects Predictive Analytics, BOPA

Overview

SparkCognition builds leading artificial intelligence solutions to advance the most important interests of society. We help customers analyze complex data, empower decision making, and transform human and industrial productivity with award-winning machine learning technology and expert teams focused on defense, IIoT, and finance.

SparkCognition

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

Hunt Oil, Hitachi High-Tech Solutions

mBank

Buyer's Guide

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