Build, scale, and deploy
AI/ML applications.
Any stack, any cloud.

The developer-friendly, IT-approved platform for AI engineers.
Get started quickly. Like in 3.5 minutes kind of quick.

Start for free

100,000+ Data Scientists and AI/ML Engineers use Saturn Cloud

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.

Saturn Cloud at a glance

Step 1: Configure credentials for data access and Git

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.

Step 2: Specify the libraries your code needs

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..

Step 3: Develop in the cloud with JupyterLab, PyCharm or VS Code

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.

Step 4: Deploy your work as a deployment or a job

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.

Saturn Cloud is available on

Install securely on your VPC. Talk to an engineer to learn more.

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

Learn More

Loved by Engineers and AI Leaders

Maciej Buckcznski
Technology Director, Darkhorse Analytics
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.
Daniel B
Lead Course Designer
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.
Greg Ringering
Quantitative Investment Research, Advent Health
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.
Seth Weisberg
Principle ML Scientist, Senseye
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.