We performed a comparison between Google Cloud Datalab 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.
"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."
"Google Cloud Datalab is very customizable."
"The APIs are valuable."
"All of the features of this product are quite good."
"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."
"The feature I like the most about Saturn Cloud is that it has lightning-fast CPUs."
"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."
"It didn't take long to see that Saturn Cloud could scale with my needs, providing more resources when required."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"The interface should be more user-friendly."
"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."
"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."
"The product must be made more user-friendly."
"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."
"Providing more detailed and beginner-friendly documentation, especially for advanced features, could greatly enhance the user experience."
"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."
"Public Clouds integration and sandbox environments would be a true game changer."
Google Cloud Datalab is ranked 15th in Data Science Platforms with 5 reviews while Saturn Cloud is ranked 8th in Data Science Platforms with 5 reviews. Google Cloud Datalab is rated 7.6, while Saturn Cloud is rated 10.0. The top reviewer of Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". On the other hand, the top reviewer of Saturn Cloud writes "Great support, good availability, and seamless integration capabilities". Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, KNIME and Qlik Sense, whereas Saturn Cloud is most compared with Amazon SageMaker and Remote Desktop with Multi-user support by Aurora. See our Google Cloud Datalab vs. Saturn Cloud report.
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We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
