Layer 3 · Hyperscale cloud
AWS, Google Cloud and Azure
The three large general-purpose clouds, each offering accelerated compute alongside everything else you already run.
Evaluated from public documentation, architecture material and source code. No vendor contact.
What it solves
Keeps accelerated compute next to the data, identity, networking and compliance controls you have already built, under contracts you already hold.
Who it suits
Organisations whose governance model is already established there and who value that integration above the lowest hourly rate.
What to watch out for
Usually the most expensive way to buy accelerator hours, and the newest hardware often reaches specialist providers first. In practice quota approval, not price, is frequently what constrains you.
Every product here gets one of these. A recommendation without a trade-off is not a recommendation.
Problems this comes up for
Worth comparing against
CoreWeave
L3 · Specialist AI cloud
A cloud built specifically for accelerated workloads at scale, rather than a general-purpose cloud with GPUs added.
- Fit
- Sustained large-scale training where committed capacity makes sense.
- Catch
- Oriented to larger commitments than intermittent workloads justify.
Lambda
L3 · Specialist AI cloud
A GPU cloud aimed at AI engineering teams, which also sells hardware for on-premises deployment.
- Fit
- Teams wanting specialist pricing, and those weighing rent against buy.
- Catch
- Popular hardware types can be capacity-constrained at times.
RunPod
L3 · On-demand and serverless compute
An on-demand accelerator platform with per-second billing and a serverless mode for inference.
- Fit
- Development, experiments, and inference that scales to zero.
- Catch
- Confirm the guarantees carefully before running anything under a strict SLA.
Wondering whether AWS, Google Cloud and Azure is the right choice?
Describe your setup and constraints. We will tell you whether it fits, and what else you should be looking at.