Build, scale, and deploy AI/ML applications with any stack,
any cloud.
The developer-friendly, IT-approved platform for AI engineers.
Get started quickly. Like in 3.5 minutes kind of quick.
Leave the infrastructure busywork behind you
We handle the backend so you can focus on what truly drives your projects forward.
Developer Freedom
Use any framework, library, or tool—no lock-in, no limits.
Experiment and Deploy
Quickly spin up environments to test new ideas, then easily deploy them into production.
Scale Without the Headaches
Scale fast—from proof-of-concept to production-ready applications.
High-Security
Avoid costly mistakes with built-in safeguards for your IP and data.
Cloud resources are expensive. If someone leaves something on when they go on vacation they could blow the monthly budget. Security is critical. One wrong move and someone can expose proprietary data and intellectual property on the internet. When individuals configure their own servers you end up with multiple ways of doing the same thing, and no deployment is reproducible. Scaling parallel jobs is hard. Dask and Ray clusters can be hard to run efficiently with fault tolerance. Code that runs in Saturn Cloud is just vanilla Python and R code. We load
your code from Git and it reads data from the same place it currently reads from. We use IAM roles when possible to
authenticate and and encrypted secrets otherwise. Everything running in Saturn Cloud runs in a Docker container. Bring your own docker images, or
customize our images with the Saturn Cloud image builder. Developing in docker means your work is much more reproducible.. Saturn Cloud Development Workspaces feel like developing on laptops, except you have much better hardware.
Workspaces support SSH so you can connect any desktop IDE like PyCharm or VS Code. Code deployed in Saturn Cloud runs in the exact same environment it was written in. Deployments can be any web application. Build dashboards with Streamlit, Shiny, Plotly, or Bokeh. Deploy models with Bento ML or FastAPI. Jobs can be any script. Use them for daily ETL or model retraining.Managing the cloud for ML teams is painful
Saturn Cloud: How it works
Step 1: Configure credentials for data access and Git
Step 2: Specify the libraries your code needs
Step3: Develop in the cloud with JupyterLab, PyCharm or VS Code
Step 4: Deploy your work as a deployment or a job
Saturn Cloud is available on
Tech specs
What's Included
- ✓Jupyter and R development environments
- ✓Support for desktop IDEs including PyCharm and VSCode
- ✓Training, fine-tuning and data processing jobs
- ✓Job Scheduling
- ✓Model deployment including endpoints for LLMs
- ✓Managed Dashboards including Streamlit, Plotly, Bokeh and Panel
- ✓Fine grained access control including IAM role support
- ✓Administrative tools, cost controls, and quotas
- ✓Installation within your cloud account and VPN
- ✓Support for restricted internet access and transparent proxies
Available Integrations
- ✓Model versioning
- ✓Model monitoring
- ✓Experiment tracking
- ✓Vector databases
- ✓Data storage and data warehouses
- ✓Enterprise identity providers and SSO
- ✓Connections to on-premise infrastructure
- ✓Networked storage
- ✓Intrusion detection and response platforms
Loved by Engineers and AI Leaders
- Saturn makes it easy for our analysts to spin up Jupyter servers of any type on demand. The flexibility in being able to define custom docker images, startup scripts, etc. is phenomenal. I love the integration with Prefect Cloud, as it lets us seamlessly run our data ingestion pipeline using the same code and in the same environment as our analysts do their work.
- Saturn Cloud makes my work so much easier. When I sit down at the beginning of the day, I just want my environment to work. I want my favorite packages installed and available on demand. I want it to be easy to scale my workspace and have it shut down automatically when I'm done. Saturn Cloud solves all of that. Their customer service is also top-notch.
- We had a portfolio backtest taking nearly 20 minutes to run before the team at Saturn Cloud took a look at it. Between advising us on our code and dispersing the work across parallel processing via Dask, this backtest was completed at a rate several hundred times faster, taking only a few seconds to execute.
- Taking runtime down from 60 days to 11 hours is such an incredible improvement. We are able to fit in many more iterations on our models. This has a significant positive impact on the effectiveness of our product, which takes many iterations to perform at the standard necessary for our customers.
Trusted by leading ML businesses
How Senseye Achieved 120x
Faster Machine Learning
Senseye uses Saturn Cloud to train machine learning models on GPUs at a massive scale.
Team Size
29
Technology
Healthcare