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Azure GPU VMs Alternatives for Training
Training a model from scratch or continuing a pre-training run, usually for days rather than hours.
What training actually needs
Why people look past Azure GPU VMs for this
The checkpoint is the cheap part to move; the built environment, the dataset cache and the exact CUDA and framework versions around it are what take a day to rebuild. That is exactly the gap Azure GPU VMs does not close: 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.
To be fair, Azure GPU VMs's real strength is real: Enterprise integration: Entra ID, Azure networking, compliance certifications, and existing enterprise agreements.
Live rates for the GPUs training wants
| GPU | From (per GPU/hr) |
|---|---|
| H100 | $2.78 |
| H200 | $3.98 |
| B200 | $9.35 |
| A100 | $1.15 |
Live per-GPU rates from Aquanode's marketplace. Refreshes every 6 hours.
How you run it here today
Run it today as a Pod: the console's own box workload card preselects the right template for this job.
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.
More alternatives pages
Other workloads on Azure GPU VMs
Training alternatives to other clouds
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.
