Google Cloud Datalab vs Saturn Cloud comparison

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Google Cloud Datalab

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Executive SummaryUpdated on Dec 5, 2024

Google Cloud Datalab and Saturn Cloud are in the data analysis and machine learning category. Saturn Cloud has the upper hand due to its advanced capabilities and enhanced workflow productivity.

Features: Google Cloud Datalab integrates smoothly with Google’s cloud ecosystem, offering comprehensive data processing, collaborative features, and extensive analysis capabilities. Saturn Cloud supports multiple programming languages and provides customization flexibility, enabling advanced data science workflows. It also offers multi-cloud compatibility and scalability, offering more extensive features.

Room for Improvement: Google Cloud Datalab could enhance its transition support for users moving from other cloud platforms and address network dependencies that limit broader deployment. Additionally, Datalab’s dashboard capabilities could be expanded to accommodate more complex visualizations. Saturn Cloud could improve by providing more comprehensive Jupyter Notebook integration for beginners, offering more accessible documentation for setting up environments, and reducing the learning curve for advanced features like Dask clustering.

Ease of Deployment and Customer Service: Google Cloud Datalab allows for quick deployment within Google's ecosystem but can be limited by dependencies on other Google tools. Saturn Cloud offers flexible deployment with both cloud and on-premises solutions and delivers notable support praised for its responsiveness and comprehensive assistance.

Pricing and ROI: Google Cloud Datalab has lower initial costs, appealing to organizations within Google's infrastructure, but may lead to higher operational expenses. Saturn Cloud involves higher upfront costs, yet its value enhances through optimal resource utilization, offering a superior ROI. Its scalable pricing models provide a cost-effective solution for growing organizations.

To learn more, read our detailed Google Cloud Datalab vs. Saturn Cloud Report (Updated: April 2025).

Google Cloud Datalab vs. Saturn Cloud

April 2025

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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

16th

Average Rating

7.8

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Visualization (18th)

Saturn Cloud

Ranking in Data Science Platforms

10th

Average Rating

10.0

Reviews Sentiment

7.5

Number of Reviews

Ranking in other categories

AWS Marketplace (14th)

Mindshare comparison

As of April 2025, in the Data Science Platforms category, the mindshare of Google Cloud Datalab is 0.9%, down from 1.2% compared to the previous year. The mindshare of Saturn Cloud is 0.2%, up from 0.1% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Featured Reviews

Nilesh Gode

Project Manager - Data Science at Mettler-Toledo International Inc.

Easy to setup, stable and easy to design data pipelines

The scalability is average. We have not faced any issues with scalability. There are more than 500 end users using this solution in our company. It is an integral part of the daily operations. The usage pattern is not a one-time thing; employees regularly access and utilize the application. We use it at a global level with a scattered user base. This means that users don't all use the application at the same time. So, around 300 out of 500 employees use the solution, and this usage is spread out throughout the day.

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Alessandro Trinca Tornidor

Sviluppatore software at TeamSystem

Good for creating POCs, training machine learning models, and experimenting without local resources

The project I’m currently working on relies on CUDA, but my local PC does not have any Nvidia GPUs. I’ve found the computational resources and ease of use provided by Saturn Cloud invaluable. Also, there are many ready-to-use Docker images and a rich documentation portal with useful examples. The dashboard for creating a new virtual environment contains almost all the features I needed: environment variable definitions, git repositories cloning directly from the new resources page, and an edit field to define a custom script during the boot process. For this reason, Saturn Cloud.io is a very good solution for creating POCs, training machine learning models, and generally experimenting a bit without worrying about local resources.

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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

"Google Cloud Datalab is very customizable."

"The APIs are valuable."

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

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

"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."

"There is plenty of computational resources (both GPU, CPU and disk space)."

"Saturn Cloud supports GPU as part of the environment, which is essential for many computational tasks in machine learning projects. It also allows us to edit the environment, including the image, before we start the cloud resources. This feature lets us quickly set up the environment without the hassle of moving the data and code to another cloud device."

"It offered an excellent development environment while not touching our production cloud resources."

"The feature I like the most about Saturn Cloud is that it has lightning-fast CPUs."

"They provide a centralized space for data, code, and results."

"It didn't take long to see that Saturn Cloud could scale with my needs, providing more resources when required."

Cons

"The product must be made 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."

"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."

"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."

"It would be nice to have more hardware category options, like TPU coprocessors or ARM64 CPUs."

"Public Clouds integration and sandbox environments would be a true game changer."

"Providing more detailed and beginner-friendly documentation, especially for advanced features, could greatly enhance the user experience."

"My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer."

"We'd like to have the capability for installing more libraries."

"Saturn Cloud should include prebuilt images for advanced data science packages like LightGBM in the next release. If possible, they should also provide a Kaggle image, which contains the most common Python packages used in machine learning."

Pricing and Cost Advice

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

"The product is cheap."

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

Information not available

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Top Industries

By visitors reading reviews

Financial Services Firm

20%

University

12%

Computer Software Company

12%

Manufacturing Company

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Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

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Questions from the Community

What do you like most about Google Cloud Datalab?

Google Cloud Datalab is very customizable.

What needs improvement with Google Cloud Datalab?

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 forcin...

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 ...

What do you like most about Saturn Cloud?

There is plenty of computational resources (both GPU, CPU and disk space).

What needs improvement with Saturn Cloud?

My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer. Users could bid on unused compute capacity, potentially leading ...

What is your primary use case for Saturn Cloud?

I'm leveraging a cloud-based platform for competitive machine learning. Tight deadlines and resource-intensive models demand powerful hardware. The cloud provides scalable GPUs and RAM, letting me ...

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Product Reports

Download Google Cloud Datalab product report

Google Cloud Datalab

April 2025

Download Saturn Cloud product report

March 2025

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

Saturn Cloud is a cloud-based data science and machine learning platform that provides a scalable, flexible, and easy-to-use environment for data scientists and machine learning engineers. Saturn Cloud offers a variety of features and tools for data science, including: Compute resources (including CPUs, GPUs, and Dask clusters), Storage (object, block, and ephemeral storage), Networking, a variety of integrations with ML tools such as JupyterLab, RStudio, and TensorFlow.

Saturn Cloud is a good choice for data scientists and machine learning engineers who need a scalable, flexible, and easy-to-use environment.

Saturn Cloud also makes it easy to collaborate with other data scientists and machine learning engineers. You can share projects, notebooks, and data with others, and you can track changes to your work.

Sample Customers

Information Not Available

Nvidia, Snowflake, Kaggle, Faeth, Advantest, Stanford University, Senseye and more.