It's not a binary decision. It's a workload classification exercise. Here's the matrix.

The cloud vs on-prem debate in enterprise AI has become a proxy war between vendors. Cloud providers want everything in their infrastructure. On-prem vendors want everything in yours. Both have financial incentives that have nothing to do with what's right for your workload.

The honest answer is that the question itself is wrong. It's not cloud or on-prem. It's: which workloads belong where, and why?

Three variables decide this for most mid-size enterprises:

Data sensitivity: Regulated data with strict residency requirements almost always points to on-prem or private cloud. Non-regulated training data is more flexible.

Load variability: Highly variable inference workloads — bursty, unpredictable — favour cloud. Steady, predictable batch workloads often favour on-prem once you model 3-year TCO.

Team capability: Cloud infrastructure requires a different operational skillset than on-prem. Be honest about what your team can actually run without burning out.

Map your workloads against these three variables before you sit in a single vendor meeting. You'll walk in with a position instead of a question — and that changes everything about how the conversation goes.