GPU Cloud Comparison: Capacity, Control, and Workload Fit

Compare Azure, Google Cloud and Hostnot GPU at the configuration and operating-model level. A vendor name does not determine model fit or availability. This worksheet is for collecting current evidence; it contains no invented hourly rates, stock guarantees or cross-provider benchmark winner.

Map the workload to an actual machine family

Azure's VM sizes overview separates GPU families such as NC, ND, NG and NV and describes compute, large-memory and graphics-oriented uses. Some Azure sizes use fractional GPUs. Do not treat every N-family VM as an interchangeable full NVIDIA accelerator for CUDA inference; inspect the exact series and size.

Google Compute Engine's GPU documentation distinguishes GPU machine configurations and their creation requirements. Check the relevant machine type, accelerator location, quota and provisioning model together. A quota allowance and actual resource availability answer different questions; neither a global product page nor a calculator guarantees a launch in your selected zone.

Hostnot GPU exposes current offerings through its GPU directory and authenticated catalog. Compare the precise GPU count, memory variant, region, CPU, RAM, disk, capacity class and environment. A grouped public row is a comparison unit, while deployment requires a current authenticated offering ID and quote.

Fill in a like-for-like evidence sheet

Dimension	Evidence to collect for each candidate
Model fit	Exact GPU memory, count, runtime support, precision and intended context/concurrency.
Topology	Within-host peer communication and any required network capability; do not infer it from count.
Capacity	Region/zone, account permission or quota, provisioning model and an explicitly authorized launch result.
Data path	Dataset location, disk persistence, transfer time and applicable storage/network charges.
Operations	Image maintenance, identity controls, monitoring, backup, incident owner and termination behavior.
Commercial terms	Currency, included resources, billing unit, commitment or interruption conditions and support scope.

Record “unknown” when a requirement is not documented for the selected offer. Do not turn an absent table cell into a claim that a provider lacks the feature. Similarly, a cloud platform's general networking or identity feature does not prove it is configured for your workload.

Compare complete costs and equivalent measurements

Use current provider calculators or quotes and retain retrieval time and all assumptions. Add only verified applicable costs; compare the same duration, region requirements, storage policy and model quality. A discounted interruptible configuration is not equivalent to a continuously available workload requirement without checkpoint and recovery analysis.

For a later authorized evaluation, pin the model revision, tokenizer, image and request distribution. Measure full-response latency, useful throughput, errors, setup time and memory. Include loading and data movement where they matter. Do not compare one provider's token streaming latency with another's completed-response latency.

Choose a recovery path before committing

Document how to export artifacts, reproduce the environment and replace unavailable capacity. Test restore separately from successful backup creation. Hostnot GPU's instance guide and quote/authorization explanation describe its supported lifecycle; consult the corresponding provider documentation for Azure and Google Cloud rather than assuming identical stop, disk or billing semantics.