Google Cloud Datalab vs SAS Visual Analytics comparison

Google and SAS are both solutions in the Data Visualization category. Google is ranked #26, while SAS is ranked #12 with an average rating of 8.8. Google holds a 1.2% mindshare in DV, compared to SAS’s 1.6% mindshare. Additionally, 80% of Google users are willing to recommend the solution, compared to 97% of SAS users who would recommend it.

Google Cloud Datalab

Read 6 Google Cloud Datalab reviews

2,400 Views
1,008 Comparison Views

80% willing to recommend

SAS Visual Analytics

Read 41 SAS Visual Analytics reviews

4,867 Views
1,898 Comparison Views

97% willing to recommend

Google Cloud Datalab

SAS Visual Analytics

Comparison Buyer's Guide

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Executive SummaryUpdated on Jan 1, 2025

SAS Visual Analytics and Google Cloud Datalab are competing in the data analytics and visualization category. Google Cloud Datalab seems to have the upper hand due to its integration with Google's cloud platform and cost-efficiency.

Features: SAS Visual Analytics is noted for its advanced statistical analysis capabilities, intuitive dashboard creation, and strong security features. Google Cloud Datalab offers seamless integration with Google Cloud services, flexible data exploration, and extensive machine learning tools.

Room for Improvement: SAS Visual Analytics could improve its cloud integration and cost-effectiveness. It might also benefit from enhancing scalability and flexibility in deployment. Google Cloud Datalab could add more robust customer support and address its limited AI configuration. Improving end-user data visualization without JSON limitations would be beneficial.

Ease of Deployment and Customer Service: SAS Visual Analytics provides a well-supported deployment process with comprehensive guidance, excelling in customer service. Google Cloud Datalab benefits from a flexible, cloud-based deployment with straightforward integration but lacks dedicated customer service, providing easier scalability.

Pricing and ROI: SAS Visual Analytics requires a higher setup cost, promising strong ROI with premium features suited for data-heavy industries. Google Cloud Datalab offers a cost-effective setup with scalable pricing, appealing to organizations seeking affordable entry points and reasonable ROI, leveraging Google’s ecosystem. The primary difference is SAS's higher initial investment versus Google Cloud Datalab’s scalable pricing model.

To learn more, read our detailed Google Cloud Datalab vs. SAS Visual Analytics Report (Updated: June 2026).

Buyer's Guide

Google Cloud Datalab vs. SAS Visual Analytics

June 2026

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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 Visualization

26th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Science Platforms (22nd)

SAS Visual Analytics

Ranking in Data Visualization

12th

Average Rating

8.2

Reviews Sentiment

5.7

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of August 2026, in the Data Visualization category, the mindshare of Google Cloud Datalab is 1.2%, up from 0.7% compared to the previous year. The mindshare of SAS Visual Analytics is 1.6%, down from 3.4% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Visualization Mindshare Distribution
Product	Mindshare (%)
SAS Visual Analytics	1.6%
Google Cloud Datalab	1.2%
Other	97.2%

Data Visualization

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.

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Namanjbaraiya Baru

Biostatistician at Lambda Therapeutic Research Ltd.

Interactive dashboards have transformed clinical reporting and now support real time decisions

The best features of SAS Visual Analytics include performing data manipulation. I would characterize this as making data ready, transforming data, and making new variables through code while utilizing low-code and no-code facilities. In my experience, low-code features in SAS Visual Analytics help when I need to create a new variable. For instance, I can extract a date through the data roll step, and with no-code features, I can perform report creation by simply using drag and drop functionality. After implementing SAS Visual Analytics, we have generated a new way to generate revenue by providing live data visuals to our clients and making our team aware of data in real time, which has had a significant positive impact.

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

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

"The APIs are valuable."

"Google Cloud Datalab is very customizable."

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

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

"The product is stable, reliable, and scalable."

"Users from the most non-technical backgrounds can use this tool to pull out valuable insights from their data."

"Visual Analytics is very easy to use."

"The correlation gives us a better understanding of new data."

"I would rate the overall solution a ten out of ten."

"Since it is simply drag and drop it makes data inspection easy."

"Overall, I think SAS VA is a great software for interactive reporting, as it is very user friendly and allows people with no statistical or computing background to learn quickly to analyse data across the company."

"Visual Analytics is very easy to use. I use Visual Analytics for all the typical use cases except text mining. I used it to analyze data and monitor statistics, not text mining. I also use it for data visualization as well as creating interactive dashboards and infographics."

More SAS Visual Analytics pros

Cons

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

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

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

"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."

"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 interface should be more user-friendly."

"If there could be 100% feasibility of forecasting feature available then we could be more accurate as it's currently 95%."

"The product is expensive and needs the integration of more languages."

"The solution should improve its graphics."

"I don't think the return on investment is high when I factor in the cost of additional components like text mining and machine learning."

"The reason we haven't rolled it out across the board is due to the fact that the licensing is so expensive."

"I haven't come across any missing features."

"The product as used was a little unintuitive and required some workarounds for tasks that should have been easy e.g. automating a query for populating a data table."

"SAS Visual Analytics could improve by making it more accessible for users outside the organization."

More SAS Visual Analytics cons

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."

"$10,000 per annum for an enterprise license."

"The product is expensive."

"It's approximately $114,000 US dollars per year."

"SAS Visual Analytics is expensive, as is the rest of the platform."

"The product is quite expensive."

"The cost of the solution can be expensive. There is an additional cost for users."

"Licensing is simple."

"It was licensed for corporate use, and its licensing was on a yearly basis."

More SAS Visual Analytics pricing and cost advice

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Top Industries

By visitors reading reviews

Construction Company

20%

Financial Services Firm

13%

Outsourcing Company

University

Financial Services Firm

14%

Construction Company

10%

Government

10%

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	13
Midsize Enterprise	10
Large Enterprise	19

Questions from the Community

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What is your experience regarding pricing and costs for SAS Visual Analytics?

My experience with pricing, setup costs, and licensing was positive, and I am happy with it.

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What needs improvement with SAS Visual Analytics?

SAS Visual Analytics offers many options, and new users unfamiliar with SAS might face some difficulties. Training on SAS Visual Analytics is required to help overcome these issues.

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What is your primary use case for SAS Visual Analytics?

My main use case for SAS Visual Analytics is making visual reports such as graphs, gauge plots, outlier plots, geomaps, and presenting my clinical data into a report or an interactive dashboard whi...

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Compared 9% of the time

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Compared 5% of the time

SAP BusinessObjects Business Intelligence vs SAS Visual Analytics

Compared 5% of the time

Verix Limelight vs SAS Visual Analytics

Compared 4% of the time

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Product Reports

Buyer's Guide

Google Cloud Datalab

July 2026

Download Google Cloud Datalab product report

Buyer's Guide

SAS Visual Analytics

July 2026

Download SAS Visual Analytics product report

Also Known As

No data available

SAS BI

Overview

Google Cloud Datalab offers an integrated environment for seamless data processing and analysis. It combines robust infrastructure with free call-up features to enhance user experience, making it a go-to choice for data-driven tasks.

Google Cloud Datalab is geared towards users seeking efficient data handling solutions. It provides a seamless setup with robust infrastructure, focusing on enhancing APIs and offering meaningful data visualization through its dashboards. Notable AI capabilities include auto-completion and data logging, although some minor configuration challenges exist. While transitioning from AWS can be complex, the platform supports dynamic data pipeline design that suits Python development, offering an end-user friendly environment.

What are the key features of Google Cloud Datalab?

Robust Infrastructure: Provides seamless setup with strong backend support.
Free Call-Up: Allows use without extra costs, enhancing accessibility.
Data Visualization: Dashboards offer meaningful insights into data trends.
AI Capabilities: Features auto-completion and data logging for efficiency.
Customizable Architecture: Adapts to different data handling needs effectively.

What benefits should users expect from Google Cloud Datalab?

Efficient Data Analysis: Streamlines exploratory data analysis in machine learning.
Enhanced API Value: API integrations add significant value to data handling.
Friendly Python Environment: Suitable for end-users looking for ease in Python development.
Adaptive Pipeline Design: Supports complex data transitions from AWS.
Global Availability: Helps overcome time lag issues with server location selection.

In specific industries, Google Cloud Datalab is instrumental in managing data analysis, machine learning exploration, and dataset preprocessing. It facilitates the transfer of workloads from AWS and ensures efficient daily data processing. Organizations benefit from its capability to provision machine learning models into Vertex AI, bolstering research and development efforts. The global availability feature plays a significant role in selecting optimal server locations, addressing time lag and connectivity challenges.

Google

SAS Visual Analytics offers rapid data processing and advanced forecasting with interactive reporting and visualization. It integrates with diverse data sources, enhancing scalability and automation, enabling data-driven decisions and extensive insight generation.

SAS Visual Analytics provides comprehensive data handling through its advanced reporting and visualization features. Businesses benefit from its ability to process data quickly and deliver insights via interactive dashboards and well-structured reports. Although it faces performance challenges with large datasets and has a complex installation process, it supports both cloud and on-premises deployments. Users can leverage its capabilities in data extraction, transformation, and loading, making it a valuable tool for finance, statistical analysis, and enterprise reporting. Despite some gaps in machine learning and integration with newer data stores, its scalability and flexibility in data management remain key advantages.

What are the most significant features of SAS Visual Analytics?

Rapid Data Processing: Efficiently handles large volumes of data for swift insights.
Interactive Reporting: Allows exploration of data through dynamic visuals.
Advanced Forecasting: Offers sophisticated prediction and analysis capabilities.
Scalable Integration: Seamlessly connects with diverse data sources.
Automation: Streamlines processes for enhanced productivity.

What benefits should users consider when evaluating?

Scalability: Ability to grow and adapt to expanding data needs.
Enhanced Insights: Delivers comprehensive report generation and visualization.
Data-driven Decisions: Empowers analytical decision-making with structured data presentation.
Flexibility: Supports integration beyond standard BI tools for broader functionality.

SAS Visual Analytics is implemented across sectors such as insurance and education for tasks like building dashboards and performing business intelligence. It is extensively used in finance and statistical analysis, turning complex data sets into actionable insights, supporting both cloud and on-premises environments.

SAS

Sample Customers

Information Not Available

Staples, Ausgrid, Scotiabank, the Australian Institute of Health and Welfare, the Blue Cross and Blue Shield of North Carolina, Oklahoma Gas & Electric, Xcel Energy, and Triad Analytics Solutions.

Buyer's Guide

Google Cloud Datalab vs. SAS Visual Analytics

June 2026

Free Report: Google Cloud Datalab vs. SAS Visual Analytics

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