Great for training ML models (uni projects)
What do you like best about the product?
It gives free GPU among other types of resources and it is very easy to use.
What do you dislike about the product?
I wish that regular users has more resources available, like more GPU per month as certain models require much more than a couple hours to train and as a result the default period of time allocated for a regular user may not be helpful in most situations.
What problems is the product solving and how is that benefiting you?
It provides resources to train ML models for university projects. It helps me because I get a platform where I can do all my work.
Strongly recommend
What do you like best about the product?
The most helpful thing about Saturn Cloud is the performance that you get for the small price. Other helpful things that I have noticed so far are how easy is to setup an environment (I am using Tensorflow on GPU and did not have to configure anything) and how fast the support is (there were 2 times when I was still using the free plan when I needed to download some datasets for training for some university projects, but the links could not be accessed from the free plan, so I have emailed the support and mentioned the issue and why enabling those links would help me and in a matter of an hour or two the issue was fixed). I am using this service weekly and so far had not any performance issues.
What do you dislike about the product?
One of the things that could be improved is the storage options. There should be a way to select any number of GBs instead of some predefined options.
What problems is the product solving and how is that benefiting you?
Saturn Cloud helps me training my AI models easier and faster than I would have done locally on my computer. Currently, I am traning an AI model for my dissertation paper and the performance of this service really helps me testing various architectures more faster and thus improving faster.
Saturn - easy training
What do you like best about the product?
When I first used Saturn Cloud, I was surprised by how effortlessly I could configure the resources needed to begin training my models. Also, the user interface is intuitive and user-friendly. The straightforward workflow allowed me to concentrate more on model development and not waste time on setting up the environment.
What do you dislike about the product?
In the past, there were 30 hours available for training and now only 10 hours are available.
What problems is the product solving and how is that benefiting you?
I need to train ML models for my bachelor's thesis and Saturn helps me to train and test more models in a very short time.
Perfect platform to train your ML models!
What do you like best about the product?
The best thing about Saturn Cloud is that creating VM is so easy, just in one click you can create a machine and start working. No hassle whatsoever!
What do you dislike about the product?
There should be a free tier with more working hours but still it's good for students and researchers like us.
What problems is the product solving and how is that benefiting you?
I needed a platform where I can train my ML models primarily for my research without worrying about the session timing out.
Fast, and poweful gpus reliable service
What do you like best about the product?
Best thinkg about daturn xloud is that it allow me to test my modules and learn about machine learning.
What do you dislike about the product?
Nothing, so far the experience is good and there is nothing that I dislike.
What problems is the product solving and how is that benefiting you?
Saturn cloud helping me deply models and execute R code which is essential to my data science learning path.
Great cloud service with GPU. 30H free
What do you like best about the product?
is its combination of stability, high-performance computing resources, ease of use, fast training environments and access to powerful computing power. Additionally, the fact that you can SSH access or have an interface similar to Jupyterlab allows you to do DL experiments very easily and quickly.
What do you dislike about the product?
Sometimes it takes forever to restart or start a Server, also de logs and GPU monitoring is very poor.
What problems is the product solving and how is that benefiting you?
Give me access to GPU processing and ready to go Docker images with all drivers and libraries for starting my research
Best deal available for students
What do you like best about the product?
The best thing is their interface which is easy to understand and use.
What do you dislike about the product?
I found out that starting a server takes a lot of time.
What problems is the product solving and how is that benefiting you?
Not all perosn has access to powerful gpus offline so they provide cheap cloud computing access
Good for creating POCs, training machine learning models, and experimenting without local resources
What is our primary use case?
Saturn Cloud provides a hosted environment where it's possible to work with various software programming tools (e.g., Jupyter Python notebooks, Julia, R and more).
The system is containerized and accessible both via Jupyter Notebook web pages and SSH—a feature that Google Colab restricts to PRO subscriptions only. I’m currently working on porting a machine learning project to CPU, which provides image Segmentation via Large Language Models. This project handles both image description, image analysis and image object segmentation. Since this project currently relies on CUDA and my local PC has no Nvidia GPUs, I’ve found the computational resources and ease of use provided by Saturn Cloud to be invaluable.
How has it helped my organization?
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.
What is most valuable?
The solution is valuable thanks to:
- plenty of computational resources (both GPU, CPU and disk space)
- a big amount of Docker image recipes
- SSH connection on free subscriptions On Google Colab, the biggest competitor in this field, this feature works only for PRO subscriptions
- possibility to personalize the characteristics of the new virtual environment directly from the dashboard page, adding new environment variables
- installing Python pip or CONDA packages and also system packages
- definition of a custom script that will be executed during the system boot process
What needs improvement?
I would like more documentation about edge and advanced use cases.
The official Docker images are only based on Debian: I would like to find official Docker images also based on other systems like Fedora or SUSE operative systems.
It would be nice to have more hardware category options, like TPU coprocessors or ARM64 CPUs.
I would like a pricing plan associated with a dedicated serverless platform specifically tailored to machine learning inference.
It would be nice to create a custom serverless API system using my own custom machine-learning model.
For how long have I used the solution?
I've used the solution for three months.
What do I think about the stability of the solution?
The service is stable, I've never experienced problems.
What do I think about the scalability of the solution?
Right now, I'm using it more for creating a POC and experimenting; I didn't try to scale up the service.
How are customer service and support?
I've never requested customer support.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
I also tried Google Colab. I switched since Colab is a little limited for normal use cases based on LLM (at the moment, disk space is only 10GB), and it restricts SSH access on PRO subscriptions.
How was the initial setup?
The initial setup is easy. It only needed to pay attention to the hardware features (e.g., I need CUDA capabilities in my example, so I chose a T4-XLarge instance with a Nvidia T4 GPU) and install Python or system dependencies. Also, pay attention to the Docker image version: an older project will need an older Docker version
What about the implementation team?
There are a good amount of official Docker images (both from StaturnCloud and third-party providers like Nvidia) but also custom Docker image by other users. I'm also satisfied with the code quality and the stability of their deployed virtual systems.
What was our ROI?
Right now, I'm using only the free plan. However, I'm evaluating an upgrade to a bigger instance (T4-4XLarge with 16 vCPU and 64GB of RAM).
What's my experience with pricing, setup cost, and licensing?
The free plan makes it a good alternative to more famous products like Google Colab, and the pricing plan is reasonable.
Which other solutions did I evaluate?
I've used and evaluated Google Colab.
What other advice do I have?
Saturn Cloud provides a good Jupyter system based on Python, Julia, or R.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Really awesome cloud compute interface for lowest price
What do you like best about the product?
Close integration with GPU compute makes development seamless. Inclusion of docker best practices is also very neat. Easy to set up and use.
What do you dislike about the product?
I don't like the inability to quickly host much like Google Colab.
What problems is the product solving and how is that benefiting you?
It's benefiting CarbonCopies because it allows us to host GPU powered docker platform very quickly.
Easy to use with good performance and collaborative features
What is our primary use case?
I'm leveraging a cloud-based platform for competitive machine learning. Tight deadlines and resource-intensive models demand powerful hardware. The cloud provides scalable GPUs and RAM, letting me experiment with cutting-edge architectures without limitations.
Its collaborative features are perfect for distributed teams, enabling seamless code sharing and analysis. I stay focused on model development, not infrastructure, thanks to the platform's streamlined setup.
My toolkit – Python, Jupyter Notebooks, and standard data science libraries – works seamlessly in the cloud environment. This ensures a smooth transition from local prototyping to large-scale competition training.
How has it helped my organization?
Saturn Cloud has become an indispensable part of my data science and machine learning toolkit. Their Dask cluster support is fantastic, allowing me to set up distributed computing with just a few clicks.
The ability to scale resources up and down effortlessly ensures my work isn't constrained by hardware and helps me control costs.
Overall, the platform provides everything I need: performance, ease of use, and a focus on the data science workflow itself. This allows me to stay focused on building solutions, not managing infrastructure.
What is most valuable?
Their Dask cluster support is a standout feature. Setting up a cluster takes mere clicks, making it incredibly simple to harness distributed computing power. I love the flexibility to scale environments up or down. This lets me optimize for performance during intensive tasks and save costs during less demanding phases. Shared folders are a game-changer for collaboration.
They provide a centralized space for data, code, and results. Also, I can set up pre-boot scripts, which would allow me to configure the instances with all the libraries I need for the particular task.
What needs improvement?
My main suggestion for improvement centers on pricing. Introducing a tier modelled after AWS spot instances would be a game-changer. Users could bid on unused compute capacity, potentially leading to significant cost savings during off-peak hours or for less time-critical tasks.
Spot instances empower users with tighter budgets or fluctuating workloads to strategically leverage lower-cost resources for development, experimentation, and background tasks. This frees up on-demand instances for truly time-sensitive work.
For how long have I used the solution?
I've used the solution for one year.
