Azure GPU VMs Alternatives for Inference
Serving or batch-running a model for generation, where throughput per dollar decides the bill.
What inference actually needs
Why people look past Azure GPU VMs for this
Little state to carry, so this is the job that moves most freely on price. The cost is the weight pull and the warm-up on the new box, not lost work. 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 inference wants
Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.
How you run it here today
Run it today with the seeded vllm-batch job recipe — a real, publicly-imaged container Aquanode can queue directly, not a template we're promising to build later.
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
Inference alternatives to other clouds
