Saturn Cloud Reviews

Name: Saturn Cloud
Brand: Saturn Cloud
Rating: 5.0 (6 reviews)

5.0 out of 5

6 reviews
100% willing to recommend

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.

Get the Saturn Cloud Buyer's Guide and find out what your peers are saying about Saturn Cloud, Amazon SageMaker, Remote Desktop with Multi-user support by Aurora and more!

Saturn Cloud is the #9 ranked solution in top Data Science Platforms and top AWS Marketplace solutions. 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.

Helped 815,854 peers since 2012

Featured reviews

Tempreviewerc C

Master Thesis at KTH | Kungliga Tekniska högskolan

One of the features I appreciate the most about Saturn Cloud is its seamless integration with Jupyter notebooks. It provides an interface that I am familiar with and use extensively. It makes it easy for me to run, track, and debug my RL models. The second feature I like is the ability to easily scale up and down the resources (like GPUs and CPUs), providing a flexible and cost-effective solution. The third feature that is useful is the availability of pre-configured environments. It saves a lot of time and hassle, especially when working with complex setups involving packages like CUDA and PyTorch.

Read full review

reviewer09751054

Tech Lead - Cloud Platforms at Nubimetrics

We use the solution for development, testing, and experiments. Here are some examples of how we use it: 1) There are some workloads that would be really uncomfortable to test in Visual Studio and require more than Google Collab and Azure Notebooks. 2) Testing that's pretty extensive and that's…

Read full review

Alessandro Trinca Tornidor

Sviluppatore software at TeamSystem

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.

Read full review

Saturn Cloud mindshare

Product category:

As of November 2024, the mindshare of Saturn Cloud in the Data Science Platforms category stands at 0.2%, up from 0.0% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Science Platforms

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.

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.

Compare Saturn Cloud with alternative products

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.