CloudGPU vs Google Cloud: Pricing, Specs, Strengths & Weaknesses

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for CloudGPU and Google Cloud. Rates sourced from public APIs — verify current availability and billing terms before purchasing.

Overview

Attribute

CloudGPU

Google Cloud

Provider type

Specialist

Hyperscaler

Founded

2008

Headquarters

Sunnyvale, CA

Billing model

On-demand (per-minute)

On-demand, Committed Use (1yr/3yr), Spot/Preemptible, Flex-start

Min commitment

None

None (on-demand)

Support tier

Community

Basic → Premium

Regions

1 regions

5 regions

Strengths & Best For

CloudGPU

CloudGPU rents on-demand GPUs billed per minute, with root SSH access and templates for Ollama, JupyterLab, ComfyUI and LLaMA-Factory. Prices come from CloudGPU's public price feed.

Strengths

Per-minute billing — pay only for what you use
Root SSH access on every instance
Ready-to-use templates: Ollama, JupyterLab, ComfyUI, LLaMA-Factory
Wide consumer and data-center GPU selection

Weaknesses

Smaller provider — limited scale vs hyperscalers
No enterprise SLAs or compliance certifications
Region information not published per instance

Best For

Developers wanting per-minute billing with no commitmentTeams running Ollama or ComfyUI workloadsCost-sensitive inference and fine-tuning

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CloudGPU Rating

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Google Cloud

Google Cloud's 2026 GPU lineup spans five accelerator-optimized machine series: A4X (NVIDIA GB200 Grace Blackwell Superchips, 720 GB HBM3e per 4-GPU node), A4 (NVIDIA B200, 1,440 GB HBM3e per 8-GPU node, 3,600 Gbps networking), A3 Ultra (NVIDIA H200, 1,128 GB HBM3e), A3 Mega/High/Edge (NVIDIA H100), and G4 (NVIDIA RTX PRO 6000 Blackwell, 96 GB GDDR7, GPU P2P communication). A2 series (A100 40/80 GB) and G2 (L4) remain available. AI Hypercomputer provides GKE and Slurm-integrated dense clusters. TPU v5 and v5e accelerators are available for TensorFlow/JAX workloads. Vertex AI provides managed ML pipelines. 30+ regions with sustained use and committed use discounts.

Strengths

A4X GB200 and A4 B200 — latest Blackwell hardware in cloud
G4 RTX PRO 6000 with GPU P2P communication for inference
AI Hypercomputer: GKE + Slurm-integrated dense clusters
TPU v5/v5e for TensorFlow and JAX workloads
Sustained use discounts and committed use contracts
Vertex AI managed ML pipelines

Weaknesses

A4X and A4 require capacity reservation — not self-serve on-demand
Premium pricing vs specialist GPU clouds
Spot (preemptible) instances can be interrupted with little notice

Best For

Frontier model training on Blackwell hardwareTensorFlow and JAX workloads on TPUsTeams using GCP services (BigQuery, Vertex AI, GKE)Inference on RTX PRO 6000 with GPU P2P

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Google Cloud Rating

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Live GPU Pricing

GPU Model

CloudGPU /hr

Google Cloud /hr

A100 40GB

$0.79

$3.67

A100 80GB

$1.59

$5.07

Green = lower price. On-demand hourly rates. Updated every 15 minutes.

Region Coverage

CloudGPU — 1 regions

Global

Google Cloud — 5 regions

us-central1us-east4europe-west4asia-east1asia-northeast1

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Provider pages

CloudGPU pricing and specs Google Cloud pricing and specs

Key differences

CloudGPU

On-demand GPUs billed per minute — RTX 3090 to A100, root SSH, Ollama and ComfyUI templates

Google Cloud

A4X (GB200), A4 (B200), A3 Ultra (H200), G4 (RTX PRO 6000) — plus TPU v5 and AI Hypercomputer

AttributeCloudGPUGoogle Cloud

Provider typespecialisthyperscaler

Billing modelOn-demand (per-minute)On-demand, Committed Use (1yr/3yr), Spot/Preemptible, Flex-start

Min commitmentNoneNone (on-demand)

Regions1 (Global)5 (us-central1, us-east4, europe-west4, +2)

Support tierCommunityBasic → Premium

Best forDevelopers wanting per-minute billing with no commitment, Teams running Ollama or ComfyUI workloadsFrontier model training on Blackwell hardware, TensorFlow and JAX workloads on TPUs

Founded / HQ · 2008 · Sunnyvale, CA