Azure GPU VMs vs Aquanode

Hyperscaler GPU VMs (NC, ND series) inside Microsoft Azure

Azure's NC and ND series make sense when your organisation is already on Azure and the GPU needs to sit inside that boundary. Priced and operated as enterprise cloud; the environment is an Azure managed disk and does not leave Azure.

Where Azure GPU VMs wins

Enterprise integration: Entra ID, Azure networking, compliance certifications, and existing enterprise agreements.
Azure Machine Learning as a managed layer above the VMs.
Reserved instances and enterprise discounting at commitment scale.

Where Aquanode wins

Your setup outlives the GPU you rented it on. Save the environment (custom nodes, model weights, packages, the config you spent an evening getting right) and bring it back up later on a completely different provider, instead of reinstalling it from scratch every session.
Supply moved, the price moved, or the region ran dry. Your environment follows you out. It is not stranded in the account that happened to create it, which is the part that makes leaving any single vendor cheap.
Pause and resume in place on every provider we support: it's a snapshot-and-terminate, then a fresh box restored from that snapshot, the same mechanism everywhere. (Voltage Park's adapter supports it too, it just has no live GPUs to rent right now.) On any provider, the same environment also comes back by restoring your saved setup onto a fresh box, a little slower, same result.
Turn on automatic snapshots yourself and pick the interval (as often as every 15 minutes, 30 by default) instead of remembering to snapshot by hand or wiring up your own cron job.
One account, one bill and one set of keys across every provider we support, rather than a separate login and invoice per vendor every time you chase capacity.
Run a saved version of your setup as a job. If a provider takes the box back mid-run, we detect the loss and queue the run to retry on a different provider, excluding the one that just lost it, and if the job checkpoints the next attempt picks up from the last one. Jobs scale to zero when the queue empties, so an idle one is not sitting on a rented GPU, and every job is bounded in the unit we bill: its time limit times its attempts times its machines is the most a single run can cost.

Azure GPU VMs pricing

We could not verify a Azure GPU VMs price first-party, so this page publishes none. Azure publishes VM rates through an interactive, region- and currency-parameterised pricing tool; the pricing page we fetched on 2026-08-05 rendered no static hourly figures. We link Microsoft's own page rather than quote a number we cannot cite first-party.

Checked https://azure.microsoft.com/en-us/pricing/details/machine-learning/ on 2026-08-05.

What we do not claim

Restoring an environment requires a snapshot that already exists. Stopping a deployment yourself captures it on the way out, so you can bring it back later on any provider. A provider-side termination is different: it is only recoverable if you had already switched automated snapshots on for that deployment, and it costs you the work since the last one. Automated snapshots are opt-in, nothing runs until you start it, and with none running there is nothing to restore.

`aq deploy --snapshot <id>` rents the cheapest matching GPU and restores your snapshot onto it, including onto a different provider than the one it came from. Creating the snapshot is a separate step today: the standalone ogre CLI on the box writes it.