# Five GPU clouds for a first pilot: building a shortlist

An editorial shortlist for a bounded LLM pilot: Runpod, Lambda, DigitalOcean, Hyperstack and AWS, with selection criteria and a matched-test plan.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/top-gpu-clouds-first-pilot/
Language: en

A first pilot should answer a model-and-infrastructure question rather than test every cloud. A small list benefits from different launch formats: a controllable compute environment, GPU VM and infrastructure within an existing ecosystem.

Current to 1 November 2026, this editorial list has no quality ranking implied by order. Inclusion has no advertising or affiliate basis. It uses official documentation without independent benchmarks.

## Inclusion method

We selected five providers with documented GPU products and identifiable technical starting points. Criteria were bounded-experiment fit, environment control and an explainable cost structure. New-account access, location and actual capacity require separate checks.

Outdated offers were not retained merely for familiar names. Nor were a dedicated physical node and ready model API compared through one numeric rate. Identify the appropriate purchase unit first.

## Five candidates and their basis

| Provider | Product to investigate | Question before launch |
| --- | --- | --- |
| [Runpod](https://docs.runpod.io/pods/overview) | Pods with your managed environment | How artifacts survive and launches repeat |
| [Lambda](https://docs.lambda.ai/public-cloud/on-demand/) | Predefined GPU VMs | Required configuration available in-region? |
| [DigitalOcean](https://docs.digitalocean.com/products/droplets/how-to/gpu/create/) | GPU Droplets with selected image | Which software stack does the model need? |
| [Hyperstack](https://docs.hyperstack.cloud/docs/virtual-machines/virtual-machine-features/) | VM with selected flavour | Which resources and terms are included? |
| [AWS](https://aws.amazon.com/ec2/instance-types/accelerated-computing/) | Suitable EC2 family | Is existing-service integration useful? |

Each row rests on the product's existence, not deployment speed measured by us. Documentation cannot determine your test result without running it.

## Reduce the list through constraints

Exclude insufficient memory, unsuitable regions and inappropriate control levels. Check account and payment requirements next. If the team cannot maintain the environment, that outweighs a small estimated rate difference.

Keep two suitably matched candidates. Five different GPUs with different models produce many numbers without a clear decision. Do not change requirements per platform merely to achieve launch success.

## Run one reproducible experiment

Use one model version and weight format, short and long context and identical requests. Measure preparation, operation, recovery and complete-period cost separately. Tie findings to configuration and date.

The result supports another test, parameter changes or a persistent-resource discussion. It does not establish future API demand. Preserve unresolved questions rather than declaring a winner from one convenient metric.

## Apply the shortlist to owned hardware

A cloud pilot can identify requirements for your GPUs. Site and operations remain separate assessments.

Read the [benchmark method](/journal/inference-benchmark-method/) and [current-shortlist method](/journal/gpu-cloud-shortlist-method/).