Very helpful support, easy to use, transparent prices, and great compute resources
What do you like best about the product?
I love that you can build your own images, spin up pcs of your own configuration. Additionally, the platform is amazingly easy to use.
The support answers quickly - I had my problems solved within 1-2 hours always.
What do you dislike about the product?
I had to build my own image for python 3.10, but that was no problem at all, with the help of the support.
The default shut-down timer is at 1 hour, which I didnt see at first so watch out for that.
What problems is the product solving and how is that benefiting you?
Training Reinforcement Learning algorithms on GPUs for my Master's thesis. Solely using CPU is not feasible in my case and my own laptop is not able to do this computation.
Best service for fast scaling and training for teams!
What do you like best about the product?
The part I love the most - how easy it was to configure everything and start training. Intuitive UX and nice docs!
What do you dislike about the product?
Nothing yet. I have not met anything that I dislike
What problems is the product solving and how is that benefiting you?
Scaling of training and optimization processes
One of the best data science platforms
What do you like best about the product?
available to use always.
data never gets deleted after server shutdown.
ssh connection
What do you dislike about the product?
Nothing yet, it's a very friendly and easy to use environment
What problems is the product solving and how is that benefiting you?
limitations and electric consumption of local PC
The solution provides prebuilt images that allow us to quickly spin up a readymade Python environment
What is our primary use case?
We use Saturn Cloud to perform data analysis on large volumes of data. Saturn fetches and updates data, We can use it for machine learning training and prediction, and perform experimental work on various data using multiple machine learning techniques. In some cases, parallel computation is also required to perform the analysis as quickly as possible.
The environment has eight CPU cores and 64 GB RAM. In some cases, we are using GPU. The development environment includes Python, Scikit Learn, XGBoost, Jupyter Lab, and Jupyter Notebook.
How has it helped my organization?
Saturn Cloud provides prebuilt images that allow us to quickly spin up a readymade Python environment, speeding up the setup of the environment required for data science projects.
Saturn Cloud also provides interactive development environments, such as Jupyter Lab and Jupyter Notebook, enabling faster code editing. Saturn's Cloud environment offers high availability and reliability, improving the efficiency of our development work.
What is most valuable?
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 solution supports Jupyter Lab and Jupyter Notebook environments as direct options. We can simultaneously work with Saturn Cloud in both modes, enabling faster interaction and sophisticated development support.
What needs improvement?
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.
I would like the option to check or uncheck the data science subpackages we need in the environment. Usage reporting should be more precise and quantify use in the number of minutes instead of just hours.
For how long have I used the solution?
I have used Saturn Cloud for more than a year.
What do I think about the stability of the solution?
Saturn Cloud provides reliable and available resources most of the time.
What do I think about the scalability of the solution?
The computation and memory can be easily scaled from the current requirement.
Which solution did I use previously and why did I switch?
Our previous solution lacked fast interactive environments like Jupyter Lab and Jupyter Notebook.
What was our ROI?
Which other solutions did I evaluate?
We also considered Paperspace Gradient and Kaggle.
What other advice do I have?
I rate Saturn Cloud 10 out of 10.
Compare to Google colab Saturn cloud is much worth with amazing computational power
What do you like best about the product?
Great thing about saturn cloud is , its amazing computational power for big data , machine learning .
I used both google colab Pro and Saturn cloud free hours and winner is Saturn cloud .
1) No need to reinstall on each kernal start
2) Very quick project running
3) High RAM access for big data analysis
4) GPU is powerful
What do you dislike about the product?
If saturn cloud provided , data folder sharing in free hours , it was amazing .
But even in free we can download data .
So thanks to saturn cloud for amazing service
What problems is the product solving and how is that benefiting you?
Saturn cloud providing high computing power with GPU access . This will help in solving and learning some advance Machine learning and Bioinformatics tasks free for students.
very good cloud experience
What do you like best about the product?
cloud environment experience and good computing power
What do you dislike about the product?
may be pricing more expensive, may lost some files from storage randomly
What problems is the product solving and how is that benefiting you?
delete file from my workspace randomly
Best cloud service for machine learning projects!
What do you like best about the product?
It runs on the remote server even when the connection is lost! You can get back to it when you have an internet connection. Your progress won't be lost. The other thing I like about saturn cloud is the powerful gpu they provide. It is twice as fast as the cloud services I previously used. Plus, the feature that alows direct access to my github repo by fetching the project structure as it is, I love it! It made it easier to work on my projects on the go.
What do you dislike about the product?
The storage is too small to upload large datasets. It usually doesn't have enough space to save the processed datasets. Would be much better if they can resolve this problem.
What problems is the product solving and how is that benefiting you?
It resolved my demand for high-performance cloud GPU. It is saving me time significantly! In addition to that, My performance increased by double when it is compared to my previous experience.
Unlocking Cloud Potential: Embracing Saturn Cloud's Seamless and Efficient Platform
What do you like best about the product?
I have been using Saturn Cloud for several months now, and I have to say, I'm impressed with how simple and efficient it is. The fundamental unit of work in Saturn Cloud is the resource, and four types are available: Python Server, R Server, Deployment, and Job. These resources are designed to handle specific tasks, from development to deployment and data pipelines.
One of the best things about Saturn Cloud's resources is its simplicity. Each resource comprises a hardware configuration, a Docker image, some initialization scripts, git repositories, and secrets used to access data. This makes the resources transparent, so anything running in Saturn Cloud can also be run outside.
The Python and R workspaces are conducive as they host Jupyter and R Studio, respectively. SSH integration connects PyCharm, VS Code, or any other IDE to the workspace. These workspaces run on Docker images, and every workspace gets a persistent EBS volume attached so that anything users write to the home directory is preserved across restarts.
Deployments and jobs are designed for non-interactive servers, with deployments used to serve models, APIs, dashboards, and jobs used for data pipelines. Deployments and assignments have almost all the same configuration options as workspaces, so anything you develop within a workspace can be run as a deployment or job.
Saturn Cloud also offers attachments, such as images, Git repositories, secrets, IAM roles, Dask clusters, and shared folders, which can be attached to any resource. This flexibility allows users to convert workflows from one resource type to another easily.
Overall, Saturn Cloud's architecture is simple, efficient, and easy to use. I highly recommend it to anyone looking for a reliable and versatile cloud service for their development, deployment, and data pipeline needs.
What do you dislike about the product?
While initially found the Hosted Pro Pay as You Go rates confusing and the costs somewhat expensive, I have to say that the benefits of Saturn Cloud's resources have made up for it. The different resource types, such as workspaces, deployments, and jobs, have been incredibly helpful in streamlining my workflow, and the service's ease of use and flexibility have been invaluable.
That being said, it would be helpful if Saturn Cloud could provide more transparent pricing and more precise communication around the costs associated with using the service. While I understand that the hosting prices are necessary to charge for the underlying AWS resources, having more visibility into these costs and how they relate to the Saturn Cloud price would be helpful.
Overall, I appreciate the functionality and simplicity of Saturn Cloud's architecture. While there is room for improvement in the pricing structure, I would still highly recommend the service to anyone looking for a reliable and versatile cloud service for their development, deployment, and data pipeline needs.
What problems is the product solving and how is that benefiting you?
Saturn Cloud simplifies and streamlines cloud-based workflows by offering a transparent, resource-based architecture that supports versatile workspaces, persistent data storage, and seamless integration of various attachments. This efficient and user-friendly platform benefits users by enhancing their development, deployment, and data pipeline experiences.
Saturn Cloud: A Game-Changer for Data Science and Machine Learning
What do you like best about the product?
Saturn Cloud is an excellent platform for data science and machine learning, with automation of DevOps and ML infrastructure engineering that frees data scientists to focus on analytics. The use of Dask to natively scale Python with advanced parallelism makes it easier to handle big data using familiar Python libraries. Cloud-hosted Jupyter notebooks allow teams to collaborate and work on projects in real-time, while high RAM and compute, GPUs, and distributed Dask clusters offer significant data processing capabilities. The ability to deploy models and dashboards using REST APIs facilitates easy sharing of work. The platform's compatibility with Python, R, or other languages and IDEs like VSCode and PyCharm make it versatile. Overall, Saturn Cloud is a game-changer for data science and machine learning, offering automation, scalability, collaboration, and compatibility.
What do you dislike about the product?
One area where Saturn Cloud could improve is by building a broader community around the platform. While Saturn Cloud is an excellent tool for data science and machine learning, having a community of like-minded individuals could make it even more powerful.
What problems is the product solving and how is that benefiting you?
Saturn Cloud is solving the problem of managing and scaling complex infrastructure required for data science and machine learning.
It has helped me get my research done for paper submission
What do you like best about the product?
Its ability to connect to my local vs code and its ability to work like a jupyter server instead of just serving a jupyter notebook
What do you dislike about the product?
The way that I don't support vscode in the jupyter server in the web version.
What problems is the product solving and how is that benefiting you?
No software needs to be installed on our computer in order to function as at home or the university, regardless of the hardware that is available.
Because we don't always use the same computers or because some people don't have a computer powerful enough to install their program locally, this is a real benefit in the context of our studies. They can access them online thanks to Saturn cloud.
