We performed a comparison between Amazon SageMaker and Saturn Cloud based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."We've had no problems with SageMaker's stability."
"They are doing a good job of evolving."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"Allows you to create API endpoints."
"We were able to use the product to automate processes."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"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 didn't take long to see that Saturn Cloud could scale with my needs, providing more resources when required."
"The feature I like the most about Saturn Cloud is that it has lightning-fast CPUs."
"It offered an excellent development environment while not touching our production cloud resources."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"Lacking in some machine learning pipelines."
"The solution is complex to use."
"SageMaker would be improved with the addition of reporting services."
"The product must provide better documentation."
"I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"The documentation must be made clearer and more user-friendly."
"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."
"We'd like to have the capability for installing more libraries."
"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."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while Saturn Cloud is ranked 8th in Data Science Platforms with 5 reviews. Amazon SageMaker is rated 7.4, while Saturn Cloud is rated 10.0. The top reviewer of Amazon SageMaker writes "Easy to use and manage, but the documentation does not have a lot of information". On the other hand, the top reviewer of Saturn Cloud writes "Great support, good availability, and seamless integration capabilities". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Microsoft Azure Machine Learning Studio, whereas Saturn Cloud is most compared with Remote Desktop with Multi-user support by Aurora. See our Amazon SageMaker vs. Saturn Cloud report.
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