Alternatives to AWS for GPU compute.
Hyperscalers are the default starting point for most teams and, for sustained GPU workloads, among the most expensive places to stay. Here is the honest comparison.
What decides this requirement.
| Stay put | Specialist cloud / reserved bare metal | Own outright | |
|---|---|---|---|
| Sustained cost | Highest at steady utilisation | The sustained rate | Lowest, if it runs hard |
| Flexibility | Highest | Term-bound, negotiable | Lowest |
| Residual risk | None | Supplier holds it | On your books |
| Control and access | Shared platform | Dedicated capacity | Total |
The short verdict. A horizon under a quarter stays on consumption pricing. Two forecastable quarters means pricing reserved bare metal against it. Sovereignty or control requirements point at owning, priced landed.
Pricing ranges from published provider pricing and the GetDeploying GPU Price Index, 2026. Hyperscaler premium from Compute Exchange, Best GPU Rental Platforms 2026. Provider capabilities from their own published documentation. Availability and rates move constantly, so treat every figure as indicative.
Flexibility you may not be using.
Published comparisons have put hyperscaler pricing for equivalent H100 capacity at roughly 86% above dedicated GPU clouds. That gap is not about the silicon, which is identical; it is the platform, integration and elasticity layered around it.
For genuinely spiky workloads inside an existing AWS estate, that premium buys something real: managed services, IAM integration, and the ability to scale to zero. Teams using those things are getting value.
For a sustained training or serving baseline, it buys flexibility that is never exercised. That is the specific situation where moving workloads off a hyperscaler produces immediate and large savings.
Less than it looks, more than nothing.
The genuine costs of moving are data egress, integration work where you depend on managed services, and operational change. Those are real and worth pricing honestly rather than dismissing.
Against them sits a cost gap that compounds every month. In our experience the payback period for a sustained baseline is short enough that hesitation costs more than the migration does.
The realistic design is rarely all-or-nothing: keep what genuinely benefits from the hyperscaler, move the baseline. The reasoning is in cloud versus bare metal.
Start from what is verified.
Current verified lines with quantities, lead times and indicative pricing are public on the live inventory. Anything not listed becomes a sourcing requirement with a first pass inside one business day.
Asked first, answered straight.
Published comparisons have put hyperscaler H100 pricing at roughly 86% above dedicated GPU clouds. The silicon is the same; the difference is the platform layer around it.
Managed service integration, IAM and tooling familiarity, and instant elasticity. Those matter for some workloads and are barely used by a steady training baseline.
It is a real one-off cost worth pricing honestly. Against a cost gap that compounds monthly, it usually pays back quickly for sustained workloads.
Rarely. Keep what genuinely benefits from the hyperscaler and move the predictable baseline. Hybrid is the normal answer, not a compromise.
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