# AWS, Google Cloud or Azure: comparing GPUs for LLMs

Compare AWS, Google Cloud and Azure for an owned model using GPU families, regions, access, connected services and a matched end-to-end application test.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/aws-vs-google-cloud-vs-azure-gpu/
Language: en

All three major clouds offer GPUs, but they are not interchangeable. Configuration, regional access, account terms and application dependencies matter. A useful comparison answers your deployment question rather than selecting the best cloud in general.

Current to 1 November 2026, this guide uses official documents. We conducted no independent benchmark and assign no speed, price or reliability rankings.

## Method: the same task and explicit constraints

Fix model, weight format, memory, context and request profile. Define acceptable regions, data requirements and management level, then select appropriate instances.

Do not compare arbitrary GPU VMs. Show configuration differences. Findings apply to the selected task and period. Future large-resource availability needs separate confirmation even after a small-machine test succeeds.

## Three starting points

| Cloud | Official-catalogue check | Team question |
| --- | --- | --- |
| [AWS EC2](https://aws.amazon.com/ec2/instance-types/accelerated-computing/) | Accelerated instance family and size | Which existing AWS components does the model need? |
| [Google Cloud](https://docs.cloud.google.com/compute/docs/gpus/about-gpus) | Compute Engine GPU configuration | How do resource, quota and zone connect? |
| [Azure](https://learn.microsoft.com/en-us/azure/virtual-machines/sizes/gpu-accelerated/nc-family) | Compute VM series and size | How do application access and network fit? |

These questions are not exclusive cloud properties. They start investigation instead of substituting general brand claims.

## Region and purchase format

Google publishes a [GPU locations map](https://docs.cloud.google.com/compute/docs/regions-zones/gpu-regions-zones?hl=en); check applicable availability for the others too. Quota, catalogue entry and actual launch are different evidence. Do not schedule a critical stage from a public device list alone.

Budget compute, storage, networking and support. [Azure pricing](https://azure.microsoft.com/en-us/pricing/details/virtual-machines/linux/) illustrates product-specific VM conditions. Compare long commitments and interruptible modes by their restrictions, not illustrative discounts alone.

## An existing application scenario

Suppose a company already uses one cloud for data and identity. It tests nearby GPU compute and compares migration elsewhere, measuring the entire request path, model, recovery and environment preparation.

If another option speeds computation but adds network delay or engineering effort, include that in the conclusion. An existing ecosystem is an argument, not a reason to hide clear model-resource incompatibility. Produce a common report with verified limitations.

## Compare owned GPUs

A pilot helps specify an owned server. Separately include placement, power, availability and maintenance. A VM's retail price is not future equipment-owner payment.

Read the [benchmark method](/journal/inference-benchmark-method/) and [buying versus renting](/journal/gpu-purchase-vs-rental/).