Anaconda Business vs SAP Predictive Analytics [EOL] comparison

Anaconda and SAP are both solutions in the Data Science Platforms category. Additionally, 96% of Anaconda users are willing to recommend the solution, compared to 100% of SAP users who would recommend it.

Anaconda Business

Read 29 Anaconda Business reviews

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2,354 Comparison Views

96% willing to recommend

SAP Predictive Analytics [EOL]

Read 3 SAP Predictive Analytics [EOL] reviews

100% willing to recommend

Anaconda Business

SAP Predictive Analytics [EOL]

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Executive SummaryUpdated on Apr 15, 2026

Anaconda Business and SAP Predictive Analytics [EOL] compete in the data analytics space. Anaconda Business has a pricing advantage due to its open-source nature, while SAP Predictive Analytics [EOL] offers more comprehensive features, providing potentially greater value.

Features: Anaconda Business is recognized for its open-source flexibility, which allows for extensive customization. It provides a wide library of data science packages and supports rapid innovation in Python and R environments. SAP Predictive Analytics [EOL] offers automated analytics capabilities, model management, and seamless integration with enterprise systems.

Ease of Deployment and Customer Service: Anaconda Business allows straightforward deployment with comprehensive support, ensuring smooth system integration. SAP Predictive Analytics [EOL] requires more effort during setup but offers robust customer service to address complex enterprise needs.

Pricing and ROI: Anaconda Business offers a cost-effective solution, leveraging its open-source platform and lower initial costs for rapid ROI. SAP Predictive Analytics [EOL] involves higher initial expenses, but its advanced features are viewed as providing substantial long-term ROI through better analytics and integration capabilities.

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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

Anaconda Business

Average Rating

8.2

Reviews Sentiment

6.8

Number of Reviews

Ranking in other categories

Data Science Platforms (7th)

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

reviewer2775498

tester at a tech vendor with 10,001+ employees

Isolate environments and switch package versions efficiently for smoother testing workflows

Overall, it works well, but there are a few things that could be better. Sometimes the environment creation or package installation feels a bit slow, especially with bigger libraries. Another thing I would appreciate is a cleaner, more intuitive interface for managing environments. It works, but a smoother UI could make the workflow faster. It would also be nice to have clearer error messages when something fails, so it is easier to understand what went wrong without digging too much. The documentation could be a bit clearer, especially for troubleshooting specific errors or setup issues. Sometimes I need to search extensively to find the exact steps. Also, having quicker or more detailed support responses would help when something unexpected comes up. These are not major problems, but improving them would definitely make the overall experience smoother. One small improvement I would add is smoother integration with IDEs. It works fine right now, but having even tighter or more automated syncing with tools such as VS Code or PyCharm would make the workflow faster. Perhaps also a few more built-in examples or quick-start guides for common setups would be helpful. Nothing major, just things that would make the experience even more user-friendly.

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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

"The most valuable feature is the set of libraries that are used to support the functionality that we require."

"The solution's most valuable aspects include the repositories, the way we log, the way we install that repository management, and how we can easily integrate the solution with Jupyter Notebook."

"The virtual environment is very good."

"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."

"It's one of the best tools available out there."

"We find the advanced security, governance, and collaborative features for organizations using Python and R particularly useful, as these business plans provided by Anaconda Business have been very helpful."

"The best part of the solution is the virtualization, where you can use Python within the virtual environment and do lots of useful things, and the documentation is excellent with a very large and active community that supports it."

"Anaconda Business offers excellent visualization tools including Visual Studio, comprehensive analysis tools, and Python language analysis that significantly helps with building analysis and machine learning models."

More Anaconda Business pros

"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."

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

"Anaconda could benefit from improvement in its user interface to make it more attractive and user-friendly. Currently, it's boring."

"A lot of people and companies are investing in creating automated data cleaning and processing environments. Anaconda is a bit behind in that area."

"When you install Anaconda for the first time, it's really difficult to update it."

"Sometimes the environment creation or package installation feels a bit slow, especially with bigger libraries."

"The solution would benefit from offering more automation."

"The pricing should be a little lower for a single person to use, as it might be affordable for an organization, but for my single use, it is difficult."

"It also takes up a lot of space."

"I have not fully explored it yet, so I can only give it an average rating."

More Anaconda Business 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

"The tool is open-source."

"The product is open-source and free to use."

"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."

"Anaconda is free to use, but in terms of hardware costs, you might need heavy GPUs to run CUDA and other demanding tasks."

"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

17%

University

11%

Manufacturing Company

Computer Software Company

Construction Company

19%

Outsourcing Company

10%

Manufacturing Company

Hospitality Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	2
Large Enterprise	19

No data available

Questions from the Community

What is your experience regarding pricing and costs for Anaconda?

My experience with pricing, setup cost, and licensing is that it is a little costly, but it is useful.

See all answers

What needs improvement with Anaconda?

I believe Anaconda Business can be improved in terms of performance and speed, particularly regarding the installation process and efficiency to avoid system freezing.

See all answers

What is your primary use case for Anaconda?

I have been using Anaconda Business for a couple of years since 2022 when I started data analysis, utilizing it for Python programming to query and analyze my data. My main use cases for Anaconda B...

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Compared 6% of the time

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Compared 5% of the time

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Product Reports

Buyer's Guide

Anaconda Business

June 2026

Download Anaconda Business product report

Buyer's Guide

Data Science Platforms

May 2026

Download SAP Predictive Analytics [EOL] product report

Also Known As

No data available

SAP BusinessObjects Predictive Analytics, BusinessObjects Predictive Analytics, BOPA

Overview

Anaconda Business provides a comprehensive platform for data science applications, integrating extensive libraries and seamless Python and R compatibility, enhancing developer productivity.

Anaconda Business offers data science professionals a platform combining extensive library support with pre-built models and seamless integration of Python and R environments. With features like a user-friendly interface and integrated Jupyter Notebook, it facilitates real-time code execution and debugging. Environmental management is simplified via Conda, while cloud-based access and package management enhance user experience. Community support and integration with applications like RStudio and Jupyter aid in data science and deep learning tasks.

What are the key features of Anaconda Business?

Extensive Library Support: Access numerous libraries for enhanced development capabilities.
Seamless Python and R Integration: Foster productivity through combined language support.
Cloud-Based Access: Facilitate collaboration and accessibility across teams.
Jupyter Notebook Integration: Enable real-time code execution and debugging.
Conda Environment Management: Simplify setup and management of environments.

What benefits should be considered in Anaconda Business?

Efficient Development: Streamline workflows with comprehensive tool integration.
Enhanced Collaboration: Utilize cloud-based tools for teamwork and data sharing.
Scalability: Adapt to growing workloads with robust capabilities.
Community Support: Leverage community expertise for solutions and troubleshooting.

Anaconda Business is widely used in industries like machine learning and data analysis, where it's employed for tasks such as predictive modeling and data visualization. Organizations utilize its compatibility with tools like Scikit-learn and TensorFlow for creating statistical models, supporting applications in fields such as analytics, education, subrogation, and warehouse management.

Anaconda

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

LinkedIn, NASA, Boeing, JP Morgan, Recursion Pharmaceuticals, DARPA, Microsoft, Amazon, HP, Cisco, Thomson Reuters, IBM, Bridgestone

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