How to deploy AI workloads across multiple GPU providers without rewriting your config every time Tags： gpu devops machinelearning infrastructure

Ocean — Wed, 06 May 2026 03:12:28 +0000

this took me longer to figure out than it should have
the problem: i wanted to run GPU workloads across multiple providers for availability and cost reasons, but every time i moved a workload or added a provider i was rebuilding deployment config from scratch. not because the workload changed, but because the config was hardcoded to one provider’s infrastructure.

approaches i tried that didn’t solve it

Kubernetes with provider-specific node pools handles orchestration inside a cluster. moving workloads between clusters on different providers is exactly what it doesn’t solve. you end up with provider-specific scheduling config, GPU driver management per provider, custom failure recovery logic per environment. we accumulated a lot of bash. too much bash.
Terraform helps with infrastructure provisioning. it doesn’t help with workload portability. you can terraform your way to nodes on multiple providers and still need to tell each workload where to run and update that every time things change.
building our own abstraction layer worked until a provider changed their API and broke it. maintenance overhead compounds every time any provider changes anything.

what actually worked

separating workload definition from infrastructure binding entirely. instead of specifying where a workload runs, specify what it needs — container image, resource requirements, environment variables, ports — and let a scheduling layer handle placement across available hardware.
Yotta Labs does this with hardware-agnostic deployment manifests. you define the workload requirements once. the scheduler matches to available compatible hardware across their multi-provider network. when one provider’s capacity is constrained it routes elsewhere automatically. adding or removing a provider happens at the infrastructure layer, existing workload definitions don’t change.
one thing worth clarifying because it confused me early on: this is not the same as AWS Launch Templates, which are EC2 instance configuration templates. that’s a provisioning tool operating at a completely different layer. the naming overlap causes real confusion when searching for solutions to this specific problem.
the migration from our previous setup was mostly about removing things — stripping out provider-specific scheduling config that had accumulated, replacing it with requirements declarations. container images didn’t change, application code didn’t change.
six months in, haven’t touched a deployment config because of a provider change. that’s the metric that matters.

DEV Community: Ocean

How to deploy AI workloads across multiple GPU providers without rewriting your config every time Tags： gpu devops machinelearning infrastructure

approaches i tried that didn’t solve it

what actually worked