Dedicated AI Infrastructure Cost vs Public Cloud Compared

Dedicated AI infrastructure cost vs public cloud is a break-even analysis: at low utilization and bursty workloads, public cloud wins on flexibility; at high sustained utilization, dedicated wins on effective cost per productive hour — and the break-even point depends on utilization, workload pattern, and operations cost. For the GPU pricing model comparison, see compare GPU pricing models. For the TCO framework, see GPU cost per hour vs TCO.

The Break-Even Analysis

Public cloud GPU: on-demand or spot, pay per hour with no commitment, elastic but with premium rates for on-demand and preemption risk for spot. Effective cost is the rate divided by utilization — and utilization is often lower than assumed because of spot preemption and scaling lag. Dedicated AI infrastructure: fixed cost for committed capacity, lower effective rate at high utilization but paid whether or not used. Effective cost is the fixed rate divided by utilization. Break-even: the utilization level where dedicated's lower rate offsets public cloud's flexibility. Above the break-even, dedicated is cheaper per productive hour; below, public cloud is. The break-even varies by GPU type, commitment term, and workload. For how to estimate per-workload, see how to estimate LLM serving cost.

Additional public cloud costs: data egress (moving data out of the cloud), storage throughput tiers, and the cost of idle resources — all add to the public cloud total. Dedicated infrastructure typically has no egress and includes storage and networking at predictable cost. Operations cost difference: public cloud requires the customer to run operations on top; dedicated with managed operations includes them. For the operations cost, see managed vs self-managed operations cost.

FAQ

Is dedicated AI infrastructure cheaper than public cloud?

It depends on utilization. At high sustained utilization, dedicated is usually cheaper per productive GPU-hour. At low or bursty utilization, public cloud wins on flexibility. The break-even is workload-specific — model it. See the analysis above and GPU cost per hour vs TCO.

Summary

Dedicated vs public cloud cost is a break-even on utilization and workload fit. Model both at your expected utilization to find the break-even. For the full cost framework, see GPU cost per hour vs TCO.