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Google Cloud GPU VMs
Hyperscaler GPU VMs (A3/A2) inside Google Cloud
What is Google Cloud GPU VMs?
Google Cloud GPU VMs is hyperscaler GPU VMs (A3/A2) inside Google Cloud. Visit Google Cloud GPU VMs's site.
What's good about Google Cloud GPU VMs
- Adjacency to BigQuery, GCS and Vertex AI, plus enterprise compliance and support
- Committed-use and sustained-use discounts at volume
- TPUs, for workloads that target them
Google Cloud GPU VMs limitations
- Rates are region-dependent and served through an interactive pricing calculator, not a static page we can cite and date
- The environment is a GCP disk image that stays in GCP
Google Cloud GPU VMs pricing
We could not verify a Google Cloud GPU VMs price first-party, so this page publishes none. Google Cloud's GPU rates are region-dependent and served through its pricing calculator; on 2026-09-19 this URL redirected to a general compute-pricing overview page that still does not render a readable per-GPU hourly table. Rather than quote a figure from a third-party aggregator, we link Google's own page. Check it for your region.
Checked https://cloud.google.com/compute/gpus-pricing on 2026-09-19.
Google Cloud GPU VMs billing
- Billing model: Per-hour, region-dependent (interactive calculator)
- Minimum commitment: None for on-demand; committed-use discounts optional
Google Cloud GPU VMs vs Aquanode
Google Cloud's GPU VMs are the right choice when the rest of your stack is on GCP: BigQuery, GCS and Vertex adjacency are the product. As a standalone GPU rental it is priced as enterprise cloud, and the environment is a GCP disk image that stays in GCP.
Where Google Cloud GPU VMs wins
- Adjacency to BigQuery, GCS, Vertex AI and the rest of the platform, plus enterprise compliance and support.
- Committed-use discounts and sustained-use pricing that meaningfully reduce the rate at volume.
- TPUs, if your workload targets them: nobody else has that option.
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, the same mechanism everywhere: a snapshot-and-terminate, then a fresh box restored from that snapshot. 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.
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.
Google Cloud GPU VMs: common questions
Why no Google Cloud prices in the table?
Their GPU rates vary by region and are published through an interactive calculator rather than a static table we can cite and date. We only publish competitor prices we have read off the vendor's own page, so this one is flagged rather than guessed.
Google Cloud GPU VMs alternatives
Other GPU cloud provider reviews
Related reading: H100 pricing and specs, H100 vs H200, A100 vs H100, H100 and H200: SXM vs NVL vs PCIe, and The best GPUs for AI, ranked.
