Amazon SageMaker vs SAP Predictive Analytics [EOL] comparison

Amazon Web Services (AWS) and SAP are both solutions in the Data Science Platforms category. Amazon Web Services (AWS) is ranked #3 with an average rating of 8.3, while SAP is ranked #27. Amazon Web Services (AWS) holds a 3.6% mindshare in Data Science Platforms, compared to SAP’s 1.4% mindshare. Additionally, 92% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of SAP users who would recommend it.

Amazon SageMaker

Read 39 Amazon SageMaker reviews

6,333 Views
3,673 Comparison Views

92% willing to recommend

SAP Predictive Analytics [EOL]

Read 3 SAP Predictive Analytics [EOL] reviews

783 Views
775 Comparison Views

100% willing to recommend

Amazon SageMaker

SAP Predictive Analytics [EOL]

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Executive SummaryUpdated on Apr 15, 2026

SAP Predictive Analytics EOL and Amazon SageMaker both compete in the machine learning space, focusing on predictive analytics and AI model deployment. Amazon SageMaker seems to have an edge due to its comprehensive cloud-native features and integration abilities.

Features: SAP Predictive Analytics EOL provides automated predictive modeling, intuitive interfaces, and simplicity in data preparation and visualization. Amazon SageMaker offers integrated Jupyter notebooks, model training and deployment, and supports multiple frameworks, making it suitable for complex projects.

Ease of Deployment and Customer Service: SAP Predictive Analytics EOL integrates well with existing SAP environments and provides unified customer support. Amazon SageMaker, being cloud-based, needs more initial setup but benefits from a strong AWS support network, offering versatile global deployment assistance.

Pricing and ROI: SAP Predictive Analytics EOL typically has lower setup costs, providing high ROI for specific markets. Amazon SageMaker has higher upfront costs but offers better long-term ROI because of extensive capabilities and adaptability, allowing businesses to utilize vast computing resources as needed.

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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

Amazon SageMaker

Ranking in Data Science Platforms

3rd

Average Rating

7.8

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

AI Development Platforms (4th)

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 Amazon SageMaker is 3.6%, down from 7.3% 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 (%)
Amazon SageMaker	3.6%
SAP Predictive Analytics	1.4%
Other	95.0%

Data Science Platforms

Featured Reviews

Saurabh Jaiswal

Python AWS & AI Expert at a tech consulting company

Create innovative assistants with seamless data integration for large-scale projects

The various integration options available in Amazon SageMaker, such as Firehose for connecting to data pipelines, are simple to use. Tools like AWS Glue integrate well for data transformations. The Databricks integration aids data scientists and engineers. SageMaker is fully managed, offers high availability, flexibility with TensorFlow, PyTorch, and MXNet, and comes with pre-trained algorithms for forecasting, anomaly detection, and more.

Read full review

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.

Read full review

Quotes from Members

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

Pros

"One of the most valuable features of Amazon SageMaker for me is the one-touch deployment, which simplifies the process greatly."

"SageMaker is a comprehensive platform where I can perform all machine learning activities."

"There is no cessation from what I can see; whatever they have in the industry, they can solve 98% of the use cases."

"Amazon SageMaker definitely provides ROI."

"Allows you to create API endpoints."

"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."

"The technical support from AWS is excellent."

"The most valuable features in Amazon SageMaker are its AutoML, feature store, and automated hyperparameter tuning capabilities."

More Amazon SageMaker pros

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

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

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

"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

"The solution requires a lot of data to train the model."

"For any cloud provider, the cost has to be substantially reduced, especially in the case of Amazon SageMaker, which is extremely expensive for huge workloads."

"Improvements are needed in terms of complexity, data security, and access policy integration in Amazon SageMaker."

"SageMaker would be improved with the addition of reporting services."

"The dashboard could be improved by including more features and providing more information about deployed models, their drift, performance, scaling, and customization options."

"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."

"Improvement is needed in the no-code and low-code capabilities of Amazon SageMaker. This would empower citizen data scientists to utilize the tool more effectively since many data scientists do not have a core development background."

"Amazon might need to emphasize its capabilities in generative models more effectively."

More Amazon SageMaker 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

"Amazon SageMaker is a very expensive product."

"I rate the pricing a five on a scale of one to ten, where one is the lowest price, and ten is the highest price. The solution is priced reasonably. There is no additional cost to be paid in excess of the standard licensing fees."

"Databricks solution is less costly than Amazon SageMaker."

"You don't pay for Sagemaker. You only pay for the compute instances in your storage."

"The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation."

"The pricing is comparable."

"There is no license required for the solution since you can use it on demand."

"The product is expensive."

More Amazon SageMaker pricing and cost advice

"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

18%

Manufacturing Company

Computer Software Company

University

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	12
Midsize Enterprise	11
Large Enterprise	18

No data available

Questions from the Community

How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

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What do you like most about Amazon SageMaker?

We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for t...

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What is your experience regarding pricing and costs for Amazon SageMaker?

If you manage it effectively, their pricing is reasonable. It's similar to anything in the cloud; if you don't manage it properly, it can be expensive, but if you do, it's fine.

See all answers

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Product Reports

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Amazon SageMaker

April 2026

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Buyer's Guide

Data Science Platforms

April 2026

Download SAP Predictive Analytics [EOL] product report

Also Known As

AWS SageMaker, SageMaker

SAP BusinessObjects Predictive Analytics, BusinessObjects Predictive Analytics, BOPA

Overview

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.

Amazon Web Services (AWS)

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

DigitalGlobe, Thomson Reuters Center for AI and Cognitive Computing, Hotels.com, GE Healthcare, Tinder, Intuit

mBank

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