Cloud vs bare metal for AI: where each one actually wins.
Every GPU cloud will tell you cloud always wins, and every bare-metal shop will tell you the opposite. We sell neither: the desk is buyer-side and paid the same whichever way your workload lands. Here is the fork as we run it for clients.
The fork in one table.
| Dimension | On-demand cloud | Reserved bare metal |
|---|---|---|
| Pricing shape | Hourly, elastic, premium for flexibility | Reserved rate, multi-year, a fraction of on-demand at sustained use |
| Performance | Virtualised or shared layers between you and the silicon | The whole node and the whole fabric, nothing between |
| Best workload | Spiky experimentation, evaluation, unpredictable demand | Sustained training runs and inference baselines |
| Queueing | Capacity when the provider has it, preemption when it does not | Your nodes, your schedule |
| Data gravity | Egress priced per byte, forever | Storage co-located with compute, moved once |
| Commitment | None, which is what you are paying for | A term, which is what you are paid for |
| Where it wins | Horizon under a quarter, demand you cannot forecast | Any workload you can see two quarters of |
The pattern in practice: teams start in the cloud because it is the right starting point, then stay two quarters too long because migration feels like a project. The bill for those two quarters typically exceeds the entire cost of moving.
Baseline reserved, burst on-demand.
Provider-specific comparisons sit alongside this: alternatives to Azure and alternatives to Google Cloud cover the hyperscaler cases in detail.
Almost no serious AI operation is purely one or the other. The stable floor of your compute belongs on reserved bare metal at reserved pricing; the unpredictable layer above it belongs wherever it is cheapest that week. Sizing that split is exactly the twenty-minute conversation this desk runs, and the follow-on question, whether the reserved layer should be leased or owned, is covered in buy versus lease.
If the answer lands on reserved capacity, the structure is on the GPU leasing desk and current verified stock is on the live inventory.
Asked first, answered straight.
When the workload becomes a baseline. On-demand pricing is built for spikes; run it flat out for months and you are paying a premium of multiples for flexibility you are not using. The tell is when the cloud bill becomes a fixed line that finance forecasts like rent.
For large distributed training, generally yes: you get the whole node, the full fabric and no virtualisation neighbours. The gap varies by workload and stack, but the bigger difference is economic, not benchmark: the same silicon at a fraction of the sustained cost.
Elastic scale-out on an hour's notice, managed services around the compute, and someone else's ops. That trade is real, which is why the answer is usually a mix: baseline on reserved bare metal, burst on-demand. The mistake is running the baseline on burst pricing.
Less than the cloud vendors suggest. Reserved bare metal in a named facility comes with the floor operated for you; orchestration on top can be yours or sourced. What you need is a team comfortable owning their scheduler rather than renting one.
Working through this on a real requirement?
Twenty minutes with the desk, no pitch and no quote at the end of it. Tell us roughly what you need and we will come back within one business day.
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