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Anaconda 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

Anaconda
Ranking in Data Science Platforms
11th
Average Rating
8.2
Reviews Sentiment
7.4
Number of Reviews
18
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 April 2025, in the Data Science Platforms category, the mindshare of Anaconda is 2.1%, down from 2.2% 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

Rohan Sharma - PeerSpot reviewer
Provides all the frameworks and makes it easy to create environments for multiple projects
The best thing is that it provides all the frameworks and makes it easy to create environments for multiple projects using Anaconda. It is easy for a beginner to learn to use Anaconda. Comparatively, it is easier than using virtual environments or other environments because of the Conda environment. However, there are many things in Anaconda that people need to be aware of, so it can be challenging.
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

"I can use Anaconda for non-heavy tasks."
"The solution is stable."
"It provides a unified platform where you can install Jupyter, Python Spider, and other related tools without needing separate installations."
"The documentation is excellent and the solution has a very large and active community that supports it."
"The most advantageous feature is the logic building."
"It's interesting. It's user friendly. That's what makes it outstanding among the others. It has a collection of R, Python, and others. Their platform strategy has a collection of many other visualization tools, apart from Spyder and RStudio, which is really helpful for data science. For any data science professional, Anaconda is really handy. It has almost all the tools for data science."
"The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors."
"The best part of Anaconda is the media distribution that comes as part of it. It gets us started very quickly."
"I was familiar with using IBM SPSS Modeler separately in the private sector before that. It's a good tool for extracting data from data warehouses, creating streams, and manipulating logic to extract final data."
"Automation is great and this product is very organized."
"So far, the stability has been rock solid."
"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."
"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."
"Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."
"It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler."
"It is a great product for running statistical analysis."
 

Cons

"I think better documentation or a step-by-step guide for installation would help, especially for on-premise users."
"A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area."
"Having a small guide or video on the tool would help learn how to use it and what the features are."
"Anaconda should be optimized for RAM consumption."
"Anaconda can't handle heavy workloads."
"When you install Anaconda for the first time, it's really difficult to update it."
"The ability to schedule scripts for the building and monitoring of jobs would be an advantage for this platform."
"One thing that hurts the product is that the company is not doing more to advertise it as a solution and make it more well known."
"I think mapping for geographic data would also be a really great thing to be able to use."
"The time series should be improved."
"Expensive to deploy solutions. You need to buy an extra deployment unit."
"Time Series or forecasting needs to be easier. It is a very important feature, and it should be made easier and more automated to use. For instance, for logistic regression, binary or multinomial is used automatically based on the type of the target variable. I wish they can make Time Series easier to use in a similar way."
"The standard package (personal) is not supported for database connection."
"I would like see more programming languages added, like MATLAB. That would be better."
"It is not integrated with Qlik, Tableau, and Power BI."
"Requires more development."
 

Pricing and Cost Advice

"The tool is open-source."
"The product is open-source and free to use."
"Anaconda is free to use, but in terms of hardware costs, you might need heavy GPUs to run CUDA and other demanding tasks."
"The licensing costs for Anaconda are reasonable."
"My company uses the free version of the tool. There is also a paid version of the tool available."
"If you are in a university and the license is free then you can use the tool without any charges, which is good."
"It is an expensive product."
"This tool, being an IBM product, is pretty 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."
"I am using the free version of IBM SPSS Modeler, it is the educational edition version."
"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 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."
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Top Industries

By visitors reading reviews
Financial Services Firm
20%
Computer Software Company
9%
Manufacturing Company
8%
University
8%
Financial Services Firm
13%
Educational Organization
12%
University
9%
Government
8%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
 

Questions from the Community

What do you like most about Anaconda?
The tool's most valuable feature is its cloud-based nature, allowing accessibility from anywhere. Additionally, using Jupyter Notebook makes it easy to handle bugs and errors.
What is your experience regarding pricing and costs for Anaconda?
Anaconda does not require a pricing structure, and it is available as an open-source tool. The features of Python, Jupyter, and others are free to use.
What needs improvement with Anaconda?
Anaconda consumes a significant amount of processing memory when working on it. This is something that needs improvement as it can impact performance.
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

LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone
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 Anaconda vs. IBM SPSS Modeler and other solutions. Updated: March 2025.
842,767 professionals have used our research since 2012.