Hybrid and multi-cloud get used interchangeably, and they shouldn't be. Hybrid means one workload estate spanning your racks and a cloud. Multi means spreading across clouds. Most AI teams need the first; fewer need the second.
The hybrid rule of thumb
If a job runs longer than a week at high utilization, it almost always wins on owned hardware in colo. Cloud wins for spikes, experiments and anything with an uncertain lifetime. Our bursting policy encodes exactly that: baseline queues stay on bare metal, overflow spills to spot automatically.
When multi-cloud earns its complexity
Running across two or three providers makes sense in exactly three cases: resilience against a regional outage, access to a scarce accelerator type, or leverage in enterprise negotiations. Each adds real operational cost — separate IAM, separate networking, separate bills to reconcile.
- Keep training data gravity in one place; move compute to data, not the reverse.
- Standardize on one Kubernetes distribution before adding a second cloud.
- Reconcile all spend — colo and cloud — in a single monthly report.
The cheapest cloud strategy is usually the simplest one you can operate at 3am: one home for steady state, one escape hatch for spikes.
How we implement it
Our hybrid cloud integration connects your cage to all three major clouds over private on-ramps, with one control plane and one invoice. Start with the whiteboard session — you keep the diagram either way.
