Guide
Why AI teams end up with GPUs in more than one place
Few teams plan a multi-cloud setup. Most arrive at one. Here’s how to make it work for you.
By the C-Gen.AI engineering team
It usually happens by accident
Credits from one provider, a reservation with another, servers bought when cloud GPUs were scarce, a customer contract that keeps data in one country. Each decision makes sense on its own. Together they leave GPUs spread across environments.
Every environment brings its own tooling
Each cloud has its own way to build a GPU cluster, its own monitoring and its own billing format. Run three environments with native tools and your team learns, maintains and stays on call for three operating models.
Some data has to stay put
Data protection rules and customer agreements can require certain data to stay in a country or on hardware you own. Keep those workloads on-premises or in the required region, and run everything else wherever GPUs are available and affordable.
Standardize the layer you touch every day
You don’t need identical hardware everywhere. You need the same scheduler, the same way to deploy, the same health signals and the same usage reports. Then choosing where a workload runs becomes a decision, not a migration project.
