Supercharge your work today
No more runtime headaches
Free up your computer by deploying projects remotely without any configuration. Enable engineers and researchers to focus on their areas of expertise instead of complicated cloud infrastructure.
Use bigger and better machines
Simply drag and drop project folders onto office workstations or cloud machines to run remotely immediately. Scale up to more powerful machines in minutes.
If it’s in a container, Galileo can deploy it
Docker containers + Galileo can handle a wide variety of software and languages. If you need help containerizing your code, we’ve got you covered.
Work smarter with collaborators
Share data sets, models, and results using any network drive or cloud storage provider. Leverage your existing file system without transferring large files, allocate resources to teammates, and track all jobs running on your machines.
Automate deployment and script against the Galileo engine
Run in parallel on one or many machines. Easy-to-use SDK to deploy hundreds or thousands of runs for sensitivity analysis or full-blown Monte Carlo simulations.
Run securely and privately
By default, no one can deploy to your computer. If you want to allow friends or colleagues to run on your machines, you can invite them, set permissions, and track all jobs. Learn more.
“Using Galileo gave me a huge productivity boost. What an incredibly simple and convenient system for running simulations and optimizations of Antora’s device design—and many at a time! It’s a real bonus that it can also run commercial software that is typically only accessed through a GUI.”Teresa DayritPython Engineer, Antora
“For us at Ad Astra, enhancing computing power is crucial. Galileo brings us the capability to easily connect up and utilize the machines we already have.”Dr. Franklin Chang DíazCEO, Ad Astra Rocket Company | Former NASA Astronaut
“The thing I really like about Galileo is I can just drag and drop jobs to multiple computers at the same time and it doesn't slow down my local machine. Years ago, I would just push my computer to its maximum capacity. I'd leave jobs running overnight. Now I don't have to do that. Galileo is doing the same thing that cloud computing did with data storage except with computing power.”Matt GasperettiCo-Founder & COO, Door USA
“I opened Galileo for the first time, ran one of my ML optimization scripts remotely with no setup, and got results delivered to my computer fast. This is a really powerful tool for anyone who needs to run experiments quickly and easily. Galileo will make it easy for my colleagues and me to run hundreds of optimizations on our machines and on cloud machines.”Abraham SarosotaMasters Candidate, Stanford Computer Science and Artificial Intelligence
“Galileo’s decentralized web of computing capability is ideal for working in an academic research environment since the system allows you to share and combine computing power within a research workgroup. Moreover, Galileo has the potential to become a computing marketplace through the Hypernet network, where the needs of those who seek computing resources can be satisfied.”Adrian GarzaData Scientist, FRISA
“Modern biomedical research imaging generates massive amount of data that can no longer be analyzed, even with the most powerful personal computers. This is where Galileo proved to be an amazing tool for data analyses. The software is very user friendly and intuitive. If you cannot afford a supercomputer and find Google, Amazon or IBM cloud computing too complicated - Galileo is something you have to consider!”Artur KirjakulovResearch Fellow, University of Southampton
How it works
Use Galileo to connect to another Galileo-enabled machine.
Next, deploy code using this simple 3-step process:
1. Set up code
2. List your assets and dependencies in a Dockerfile
3. Drag and drop to Galileo
Track your job’s status and get notified when your results arrive.
The creative drivers behind Galileo
Galileo was created by a team of Stanford physicists, computer scientists, aerospace engineers, and entrepreneurs. Our various home fields require access to large quantities of compute power for modeling simulations, and we were constantly frustrated by the difficulty and costs of deploying code to the cloud. As good engineers, we set out to fix the problem.