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Darwin vs IBM SPSS Modeler comparison

 

Comparison Buyer's Guide

Executive SummaryUpdated on Dec 5, 2024

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
23rd
Average Rating
8.0
Reviews Sentiment
6.7
Number of Reviews
8
Ranking in other categories
No ranking in other categories
IBM SPSS Modeler
Ranking in Data Science Platforms
13th
Average Rating
8.0
Reviews Sentiment
6.3
Number of Reviews
40
Ranking in other categories
Data Mining (3rd)
 

Mindshare comparison

As of August 2026, in the Data Science Platforms category, the mindshare of Darwin is 1.5%, up from 0.5% compared to the previous year. The mindshare of IBM SPSS Modeler is 3.1%, up from 2.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms Mindshare Distribution
ProductMindshare (%)
IBM SPSS Modeler3.1%
Darwin1.5%
Other95.4%
Data Science Platforms
 

Featured Reviews

AC
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.
RB
Business Owner at SASS GmbH
Support and flexibility enable effective project initiation and meet customer needs but deployment requires enhancement
The customer comes to you and says they want to deploy it and make a production out of this, which is very difficult and expensive with IBM SPSS Modeler. With MATLAB, there is no problem. I have a solution, and then I convert my MATLAB solution to C programming language. This I can deploy, and I can check it, and it is MISRA compatible. It is very easy to deploy it, to go from MATLAB to C or C++, which is actually needed in the car industry. In the car industry, they want to have it in the hardware. You cannot put MATLAB or IBM SPSS Modeler in the hardware of a car, but with C, there is no problem with a microcontroller. They can shoot it into the microcontroller, and I can check it with Polyspace, and it is MISRA compatible, which is an industrial standard. There is nothing similar in IBM SPSS Modeler. I made solutions with IBM SPSS Modeler, and then the customer said they wanted to make a production out of it, and it was not possible. I stopped with IBM SPSS Modeler 18. It is now 18.6 from what I know at the moment. I do not believe that there is a possibility to design a graphic user interface with it. It is itself a graphic user interface, where you put all sorts of little icons into the display.

Quotes from Members

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

Pros

"Darwin is really useful for people who don't necessarily do a lot of data science."
"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."
"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."
"Due to the predictions that we have been able to do because of the use of Darwin, we have decreased our delinquency index from almost nine percent to five percent and reduced our client loss index from 19 percent to 10 percent."
"In terms of streamlining a lot of the low-level data science work, it does a few things there."
"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."
"It did help us to convert data into knowledge."
"The most valuable feature is the model-generation. With a nice dataset, Darwin gives you a nice model. That's a really nice feature because, if we're doing that ourselves, it's trial and error; we change the parameters a little and try again. We save time by just giving the dataset to Darwin and letting Darwin generate a model. We find the models it generates are good; better than we can generate."
"It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it"
"It is very scalable for non-technical people."
"The visual modeling capability is one of its attractive features."
"It handles large data better than the previous system that we were using, which was basically Excel and Access. We serve upwards of 300,000 parts over a 150 regions and we need to crunch a lot of numbers."
"I would recommend IBM SPSS Modeler for people who need to do statistical analysis of this type."
"Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."
"We are creating models and putting them into production much faster than we would if we had just gone with a strict, code-based solution, like R or Python."
"We use analytics with the visual modeling capability to leverage productivity improvements."
 

Cons

"An area where Darwin might be a little weak is its automatic assessment of the quality of datasets. The first results it produces in this area are good, but in our experience, we have found that extra analysis is needed to produce an extra-clean set of data."
"Darwin is stable, but it just doesn't provide the functionality that an analyst would need."
"The Read Me's and the tutorials need to be greatly improved to get customers to understand how things work. It might be helpful to have some sample data sets for people to play around with, as well as some tutorial videos. It was very hard to find information on this in the time crunch that we had, to see how it worked and then make it work, while interfacing with folks at SparkCognition."
"We have used Darwin as a complement to other tools like R and SPSS to get the accuracy we want."
"The license cost is not cheap, especially not for markets like Mexico."
"The challenge is very big toward making models operational or to industrialize them. E.g., what we want to do is to make unique credit models for each customer. So, we are preparing the types of customers who we can try new credit models on Darwin. But, I see this still very challenging to be able to get the data sets so Darwin can work. At this point, we are working it to get the data sets ready for Darwin."
"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."
"Because so much is done under the hood of Darwin, it is hard to trust how it gets the answers it gets."
"It is very good, but slow. The slowness may be because we have not finalized all the background information in SPSS. It still needs some tweaking."
"Weak documentation and user guide."
"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."
"I would not rate the technical support very well. The technicians have accents, and when you do find someone, it is very hard to get somebody able to answer the technical questions."
"We have run into a few problems doing some entity matching/analytics using this portion of the software."
"It's very stable, although it is not as user friendly as it could be."
"The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood."
"The integration with sources and visualisation needs some improvement. The scalability needs improvement."
 

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."
"I believe our cost is $1,000 per month."
"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."
"Having in mind all four tools from Garner’s top quadrant, the pricing of this tool is competitive and it reflects the quality that it offers."
"The scalability was kind of limited by our ability to get other people licenses, and that was usually more of a financial constraint. It's expensive, but it's a good tool."
"It is a huge increase to time savings."
"Its price is okay for a company, but for personal use, it is considered somewhat expensive."
"When you are close to end of quarter, IBM and its partners can get you 60% to 70% discounts, so literally wait for the last day of the quarter for the best prices. You may feel like you are getting robbed if you can't receive a good discount."
"It got us a good amount of money with quick and efficient modeling."
"The government has funds and a budget, it's hard to say if it's expensive or cheap. In Canada, they have a yearly budget. They used to encourage people to use the modeler for development. If ten users use the server with ten licenses, it runs faster. But if forty users use the same appliance, everything slows down. People then think it's not easy to do things and prefer using remote tools like Python to extract data from the database. It's not about being expensive or cheap, but about people's knowledge and experience in how to do the work."
"It is an expensive product."
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Top Industries

By visitors reading reviews
Construction Company
18%
Financial Services Firm
14%
Manufacturing Company
10%
University
7%
Financial Services Firm
10%
Government
9%
Construction Company
9%
Outsourcing Company
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
By reviewers
Company SizeCount
Small Business6
Large Enterprise2
By reviewers
Company SizeCount
Small Business9
Midsize Enterprise4
Large Enterprise32
 

Questions from the Community

Ask a question
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What is your experience regarding pricing and costs for IBM SPSS Modeler?
The government has funds and a budget, it's hard to say if it's expensive or cheap. In Canada, they have a yearly budget. They used to encourage people to use the modeler for development. If ten us...
What needs improvement with IBM SPSS Modeler?
The customer comes to you and says they want to deploy it and make a production out of this, which is very difficult and expensive with IBM SPSS Modeler. With MATLAB, there is no problem. I have a ...
What is your primary use case for IBM SPSS Modeler?
I have been using IBM SPSS Modeler for a long time. I am using IBM SPSS Modeler mainly for ETL. Sometimes I use it to compare the results of the modeling as compared to MATLAB. MATLAB is the main t...
 

Comparisons

 

Also Known As

No data available
SPSS Modeler
 

Overview

 

Sample Customers

Hunt Oil, Hitachi High-Tech Solutions
Reisebªro Idealtours GmbH, MedeAnalytics, Afni, Israel Electric Corporation, Nedbank Ltd., DigitalGlobe, Vodafone Hungary, Aegon Hungary, Bureau Veritas, Brammer Group, Florida Department of Juvenile Justice, InSites Consulting, Fortis Turkey
Find out what your peers are saying about Darwin vs. IBM SPSS Modeler and other solutions. Updated: June 2026.
909,679 professionals have used our research since 2012.