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Saturn Cloud Reviews

Vendor: Saturn Cloud
5.0 out of 5
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What is Saturn Cloud?

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.

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Saturn Cloud is the #14 ranked solution in top Data Science Platforms. PeerSpot users give Saturn Cloud an average rating of 10.0 out of 10. Saturn Cloud is most commonly compared to Amazon SageMaker: Saturn Cloud vs Amazon SageMaker.
Buyer's Guide
Saturn Cloud
August 2025
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Saturn Cloud mindshare

As of August 2025, the mindshare of Saturn Cloud in the Data Science Platforms category stands at 0.3%, up from 0.1% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Data Science Platforms Market Share Distribution
ProductMarket Share (%)
Saturn Cloud0.3%
Databricks15.3%
Dataiku12.9%
Other71.5%
Data Science Platforms

PeerResearch reports based on Saturn Cloud reviews

TypeTitleDate
CategoryData Science PlatformsAug 29, 2025Download
ProductReviews, tips, and advice from real usersAug 29, 2025Download
ComparisonSaturn Cloud vs DatabricksAug 29, 2025Download
ComparisonSaturn Cloud vs KNIME Business HubAug 29, 2025Download
ComparisonSaturn Cloud vs Amazon SageMakerAug 29, 2025Download
Suggested products
TitleRatingMindshareRecommending
Amazon SageMaker3.96.1%91%38 interviewsAdd to research
 
 
Key learnings from peers

Valuable Features

Saturn Cloud offers a seamless integration with Jupyter notebooks, allowing users to easily run, track, and debug RL models. Another valuable feature is the ability to scale up and down resources like GPUs and CPUs, providing flexibility and cost-effectiveness.

One of the users shared with us: "The availability of pre-configured environments saves time and hassle, particularly when working with complex setups involving packages like CUDA and PyTorch".

Saturn Cloud supports GPUs, which are essential for many machine learning tasks, and allows users to edit the environment before starting the cloud resources. Additionally, Saturn Cloud provides lightning-fast CPUs and offers lots of free computing resources upfront, allowing for easy prototyping and fine-tuning of models. This can give users a head start in deployment and iterate quickly, regardless of whether they are in academia or industry.

  • "They provide a centralized space for data, code, and results."
  • "There is plenty of computational resources (both GPU, CPU and disk space)."
  • "It offered an excellent development environment while not touching our production cloud resources."

Room for Improvement

Saturn Cloud could improve the user experience by making the process of setting up custom environments more beginner-friendly. They should provide more detailed and beginner-friendly documentation, especially for advanced features. 

It would be beneficial to include prebuilt images for advanced data science packages, such as LightGBM and a Kaggle image. "The option to choose specific data science subpackages in the environment would be useful." Usage reporting should be more precise and quantify use in minutes instead of just hours.

It would be helpful if Saturn Cloud offered a wider range of libraries for installation, including support for C++. Providing a way to easily and freely host API/web apps would be advantageous, as it is a common need for data scientists to deploy web apps for their machine learning models.

Finally, the pricing should be more transparent.

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

Popular Use Cases

Saturn Cloud is primarily used for training deep reinforcement learning agents, performing data analysis on large volumes of data, and running machine learning models.

The users we spoke with rely on Saturn Cloud's environment for resource-intensive tasks, including heavy computation power like GPUs and multi-core CPUs. The platform supports and assists in creating custom images and provides a fast and efficient development environment with language support and libraries. It also offers storage for large datasets and enables quick prototyping and software iteration in the industry.

Service and Support

Saturn Cloud's customer service and support receive positive feedback for being friendly, efficient, and helpful. They are prompt in their replies, typically responding within hours. Customers appreciate that their issues are resolved quickly and with detailed explanations. The support team showcases their technical expertise by addressing complex queries, even those beyond their usual scope.

Deployment

Saturn Cloud's initial setup process is straightforward, allowing users to swiftly choose templates or initiate new servers with Python libraries. They may need additional software for a standard Linux setup. Some find custom images slightly more challenging, but assistance resolves issues quickly. Setup typically takes minutes, requiring attention to hardware features like CUDA capabilities with specific instances such as T4-XLarge. Users must also consider compatibility with Docker image versions to match project requirements.

Scalability

The solution of Saturn Cloud has "excellent" scalability, allowing machines to reach extreme configurations. It is easy to scale the computation and memory according to the current requirement, and the support provided ensures that it can scale well to meet any future needs.

Stability

Based on the feedback provided, the solution of Saturn Cloud is highly stable. Users have experienced uninterrupted service with a 100% uptime and have found the resources to be consistently reliable and available.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.
Download our Saturn Cloud Buyer's Guide for additional reliable information.

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Learn more about Saturn Cloud

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.

Saturn Cloud customers

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

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Popular Comparisons

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Amazon SageMaker vs Saturn Cloud
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Saturn Cloud Reviews Summary
Author infoRatingReview Summary
Works at a tech consulting company with 51-200 employees5.0I use Saturn Cloud for competitive machine learning, benefiting from scalable GPUs, seamless collaboration, and efficient infrastructure setup. Key features include Dask cluster support and shared folders. However, I suggest introducing an AWS spot instances-like pricing model for cost savings.
Sviluppatore software at TeamSystem5.0Saturn Cloud offers a flexible and resource-rich environment for my machine learning projects, surpassing Google Colab's limitations. I enjoy its customization features despite wishing for more diverse Docker images and documentation. Currently, I'm considering upgrading from the free plan.
Tech Lead - Cloud Platforms at Nubimetrics5.0We use Saturn Cloud for development and testing with features like Jupyter Labs Server and Dask Clustering. While it's effective, improvements like a Scala Spark Kernel and enhanced GPU offerings would be beneficial. It surpasses alternatives like Google Colab and Azure Notebooks.
Master Thesis at KTH | Kungliga Tekniska högskolan5.0I use Saturn Cloud to train deep reinforcement learning agents with Python, CUDA, and PyTorch. Its Jupyter notebook integration, resource scalability, and pre-configured environments are invaluable. Setup could be beginner-friendly, but support is helpful. Previously, I used my laptop and Colab.
Data Scientist5.0I use Saturn Cloud for data analysis and machine learning tasks, benefiting from its GPU support and environment customization options. Improvements could include prebuilt images for packages like LightGBM and more precise usage reporting.
Works5.0I use Saturn Cloud to run machine learning models quickly with extensive language support and libraries. Its fast CPUs and free resources enable efficient model tuning. Improvements could include broader library support, more language options, and easy web app hosting.