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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
27th
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
6.6
Number of Reviews
39
Ranking in other categories
Data Mining (4th)
 

Mindshare comparison

As of May 2025, in the Data Science Platforms category, the mindshare of Darwin is 0.3%, up from 0.3% compared to the previous year. The mindshare of IBM SPSS Modeler is 2.4%, down from 2.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
Data Science Platforms
 

Featured Reviews

AC
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.
PeterHuo - PeerSpot reviewer
Good tool for extracting data from data warehouses, creating streams, and manipulating logic to extract final data
There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the server due to memory leaks. This performance problem bothers people. The performance issue seems to be related to the server. We design streams on the client and submit them to the server, which generates a large SQL statement. There are two potential bottlenecks: one in the server and another in data extraction. I'm unsure about the exact mechanics of data splitting when fetching from the database. When streams become larger, performance bottlenecks may occur in the IBM SPSS Modeler server or the database. Sometimes the server crashes and needs to be restarted to release memory on both sides. I'm not sure exactly where the problem is caused, as I focus on stream design rather than server issues. The problem could be on the IBM SPSS Modeler server and database.

Quotes from Members

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

Pros

"The solution helps with the automatic assessment of the quality of datasets, such as missing data points or incorrect data types."
"The key feature is the automated model-building. It has a good UI that will let people who aren't data scientists get in there and upload datasets and actually start building models, with very little training. They don't need to have any understanding of data science."
"The thing that I find most valuable is the ability to clean the data."
"I find it quite simple to use. Once you are trained on the model, you can use it anyway you want."
"In terms of streamlining a lot of the low-level data science work, it does a few things there."
"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."
"Darwin has increased efficiency and productivity for our company. With our risk management team, there were models that took them more than three days to process each, only to see the outcome. Now, it takes minutes for Darwin to process the current model. So, we can have it in minutes. We don't have to wait three days for all the models to be tested, then make a decision."
"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."
"It scales. I have not run into any challenges where it will not perform.​"
"I think the code modeling features are the most valuable and without the need to write a code back with many different possibilities to choose from. And the second one is linked to the activity of the data preparation."
"Very good data aggregation."
"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."
"The quality is very good."
"It will scale up to anything we need."
"It is just a lot faster. So you do not have to write a bunch of code, you can throw that stuff on there pretty quickly and do prototyping quickly."
"We are using it either for workforce deployment or to improve our operations."
 

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."
"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."
"The analyze function takes a lot of time."
"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."
"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."
"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."
"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."
"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."
"The product does not have a search function for tags."
"There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the server due to memory leaks. This performance problem bothers people. The performance issue seems to be related to the server. We design streams on the client and submit them to the server, which generates a large SQL statement. There are two potential bottlenecks: one in the server and another in data extraction. I'm unsure about the exact mechanics of data splitting when fetching from the database."
"It would be beneficial if the tool would include more well-known machine learning algorithms."
"Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization."
"Customer support is hard to contact."
"I think mapping for geographic data would also be a really great thing to be able to use."
"Expensive to deploy solutions. You need to buy an extra deployment unit."
"It would be helpful if SPSS supported open-source features, for example, embedding R or Python scripts in SPSS Modeler."
 

Pricing and Cost Advice

"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."
"As far as I understand, my company is not paying anything to use the product."
"I believe our cost is $1,000 per month."
"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 am using the free version of IBM SPSS Modeler, it is the educational edition version."
"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."
"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."
"$5,000 annually."
"If you are in a university and the license is free then you can use the tool without any charges, which is good."
"This tool, being an IBM product, is pretty expensive."
"Its price is okay for a company, but for personal use, it is considered somewhat expensive."
"It is a huge increase to time savings."
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Top Industries

By visitors reading reviews
No data available
Financial Services Firm
13%
Educational Organization
12%
University
9%
Computer Software Company
9%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

Ask a question
Earn 20 points
What do you like most about IBM SPSS Modeler?
Compared to other tools, the product works much easier to analyze data without coding.
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?
There are performance issues. Extracting data from many combined tables can take hours and occasionally crash the server due to memory leaks. This performance problem bothers people. The performanc...
 

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: April 2025.
850,236 professionals have used our research since 2012.