Table of Contents
The dedicated-vs-cloud question is the most common architectural decision for AI infra teams. Here's the honest breakdown.
TL;DR
Dedicated wins on: cost predictability, data residency, full root, no preemption. Cloud wins on: elasticity, regional breadth, integration with rest of cloud. For steady production, dedicated. For spiky training, cloud.
Side-by-side
What works
- Dedicated: fixed monthly cost, no surprises
- Dedicated: full root, no virtualisation tax
- Dedicated: data residency easy (UK/EU)
- Dedicated: no preemption mid-training
- Dedicated: cheap for steady workloads
Where it breaks
- Dedicated: not elastic, can't scale instantly
- Dedicated: regional choice limited
- Dedicated: requires monthly commitment
- Cloud: per-hour billing punishes 24/7 inference
- Cloud: noisy neighbours possible
- Cloud: GPU capacity sometimes scarce
By workload
| Workload | Dedicated | Cloud |
|---|---|---|
| Steady inference (24/7) | ✓ Cheaper | ✗ |
| Spiky inference | ✗ | ✓ Pay-per-use |
| Long fine-tunes | ✓ Predictable | ✗ Per-hour adds up |
| Multi-region scale | ✗ Limited | ✓ Easy |
| Compliance / residency | ✓ Documented chain | ~ Depends on region |
Verdict
Most production AI workloads have steady traffic and benefit from dedicated. Cloud GPU for genuinely elastic workloads.
Bottom line
Match shape to traffic. See serverless vs dedicated.
