Google Cloud Datalab vs SAP Predictive Analytics [EOL] comparison

Google and SAP are both solutions in the Data Science Platforms category. Google is ranked #17 with an average rating of 9.0, while SAP is ranked #27. Google holds a 1.7% mindshare in Data Science Platforms, compared to SAP’s 1.4% mindshare. Additionally, 80% of Google users are willing to recommend the solution, compared to 100% of SAP users who would recommend it.

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

1,511 Views
982 Comparison Views

80% willing to recommend

SAP Predictive Analytics [EOL]

Read 3 SAP Predictive Analytics [EOL] reviews

783 Views
775 Comparison Views

100% willing to recommend

Google Cloud Datalab

SAP Predictive Analytics [EOL]

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Executive SummaryUpdated on Apr 15, 2026

SAP Predictive Analytics EOL and Google Cloud Datalab compete in analytics and predictive modeling. Google Cloud Datalab may have an advantage due to its modern infrastructure and cloud integration, appealing for scalable solutions despite cost considerations.

Features: SAP Predictive Analytics EOL was recognized for advanced automation in predictive modeling, strong data integration within SAP, and effective enterprise setups. Google Cloud Datalab provides flexibility, extensive library support, and seamless integration with Google's cloud services for robust analysis and scalable solutions, catering to dynamic cloud environments.

Ease of Deployment and Customer Service: SAP Predictive Analytics EOL required a tailored deployment process primarily suited for SAP environments, supported by thorough documentation. Google Cloud Datalab, with its cloud-native design, enabled smoother deployment with less on-premise configuration, complemented by Google's comprehensive support.

Pricing and ROI: SAP Predictive Analytics EOL had upfront setup costs typical of enterprise solutions, including licensing fees influencing ROI. Google Cloud Datalab, as part of Google Cloud Platform, provides flexible pricing models optimized based on usage, potentially offering faster scalable ROI.

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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

Google Cloud Datalab

Ranking in Data Science Platforms

17th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (15th)

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 Google Cloud Datalab is 1.7%, up from 0.9% 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 (%)
Google Cloud Datalab	1.7%
SAP Predictive Analytics	1.4%
Other	96.9%

Data Science Platforms

Featured Reviews

LijomonJose

System Architect at UST Global EspaÃ±a

dashboards are good and data visualization is more meaningful for the end-user

Access is always via URL, and unless your network is fast, it would be a little tough in India. In India, if we had a faster network, it would be easier. In a big data environment, like when forcing your database with over a billion records, it can be tough for the end-user to manage the data. You need to have a single entity system in each environment. It's not because of GCP, but it would be great to have options like MongoDB or other similar tools in GCP. Then, we wouldn't always need to connect to the cloud and execute SQL queries. Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated. Once the data is collected, it should be easily sorted.

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

"All of the features of this product are quite good."

"The APIs are valuable."

"For me, it has been a stable product."

"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."

"All of the features of this product are quite good."

"The infrastructure is highly reliable and efficient, contributing to a positive experience."

"Google Cloud Datalab is very customizable."

"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."

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

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

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

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

Cons

"The interface should be more user-friendly."

"The product must be made more user-friendly."

"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."

"Even if your application is always connected to its database, the processing can be cumbersome. It shouldn't be so complicated."

"The interface should be more user-friendly."

"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."

"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."

"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

"The product is cheap."

"It is affordable for us because we have a limited number of users."

"The pricing is quite reasonable, and I would give it a rating of four out of ten."

"A free trial version is available for testing out this solution."

"The pricing is reasonable"

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Top Industries

By visitors reading reviews

Financial Services Firm

21%

Construction Company

16%

University

Computer Software Company

No data available

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What needs improvement with Google Cloud Datalab?

See all answers

What is your primary use case for Google Cloud Datalab?

It's for our daily data processing, and there's a batch job that executes it. The process involves more than ten servers or systems. Some of them use a mobile network, some are ONTAP networks, and ...

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What advice do you have for others considering Google Cloud Datalab?

Overall, I would rate it a nine out of ten. Google Cloud is very good. Once you go through the features of Google Cloud, it's a good idea to get a GCP certification so you have an idea of how it ca...

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Compared 5% of the time

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Product Reports

Buyer's Guide

Google Cloud Datalab

April 2026

Download Google Cloud Datalab product report

Buyer's Guide

Data Science Platforms

April 2026

Download SAP Predictive Analytics [EOL] product report

Also Known As

No data available

SAP BusinessObjects Predictive Analytics, BusinessObjects Predictive Analytics, BOPA

Overview

Cloud Datalab is a powerful interactive tool created to explore, analyze, transform and visualize data and build machine learning models on Google Cloud Platform. It runs on Google Compute Engine and connects to multiple cloud services easily so you can focus on your data science tasks.

Google

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

Information Not Available

mBank

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