We’re architecting our AI infrastructure roadmap and stuck on a fundamental question: when does hybrid cloud make more sense than multi-cloud for GPU-intensive workloads, and vice versa?
Our current setup has baseline model training running on on-premises GPUs we own outright, with occasional bursts into AWS for peak capacity. It’s working reasonably well for cost predictability on sustained workloads. But we’re now seeing pressure to distribute across Azure and GCP as well—partly for redundancy, partly because different teams want access to different cloud-native AI services, and partly because GPU availability has been unpredictable on any single provider.
The trade-offs aren’t obvious to me. Hybrid gives us control and cost predictability for baseline load, but multi-cloud promises better availability and leverage in negotiations. On the other hand, orchestrating across three public clouds plus on-prem feels like it could become an operational nightmare. And I’m not sure our workload placement logic is sophisticated enough yet to make real-time decisions about where to run what.
Curious how others have thought through this decision. What drove you toward one model or the other? Did you end up doing both? And if you’re running multi-cloud GPU orchestration, what does that actually look like day-to-day?
One thing that helped us was mapping workloads by predictability and latency sensitivity. Predictable, sustained workloads stay on-prem on flat-rate infrastructure. Bursty experimental workloads go multi-cloud because we can chase spot pricing. Latency-sensitive inference stays close to users, which sometimes means edge, sometimes means a specific regional cloud. Once we had that mapping, the hybrid vs multi-cloud question became a lot clearer—it wasn’t either/or, it was which workload belongs where.
We went multi-cloud after getting burned on availability twice in six months. One provider ran out of H100 quota during a critical training cycle, and we had no fallback. Now we’re orchestrating across AWS, Azure, and GCP with Kubernetes federation and it’s been worth the operational overhead. Real-time price arbitrage alone has cut our GPU spend by about 40%, and we can usually find capacity somewhere even when one cloud is constrained. The key was investing in abstraction layers early—Terraform modules that translate our generic GPU requirements into provider-specific instance types automatically.
Another angle: vendor lock-in risk. If you’re heavily invested in one cloud’s AI services—like Azure’s ML stack or GCP’s TPU ecosystem—it’s harder to justify multi-cloud orchestration because you lose those integrations. But if you’re mostly running open-source frameworks on generic GPU compute, multi-cloud makes a lot more sense. We ended up using hybrid for proprietary model training and multi-cloud for general-purpose inference, which let us hedge our bets without fragmenting our toolchain too much.