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Cloud GPU VS Hinode

Compare Cloud GPU VS Hinode and see what are their differences

Cloud GPU

Cloud GPU is a solution that provides high-performance GPUs on Google Cloud for machine learning and 3D visualization.

Rating
0 reviews
Alternatives Is it good?
Hinode

Cloud workspace for AI image and video work that keeps your files and ComfyUI setup between sessions, with L4, L40S or RTX PRO 6000 machines attached by the minute.

Rating
0 reviews
Pricing
Paid $0.29 (Workspace)
Alternatives Is it good?
Seranova
Seranova AI helps home service businesses automate review outreach, stay on top of customer conversations, and grow reputation without extra headcount. sponsored

Which is more popular?

Based on our record, Cloud GPU seems to be more popular. It has been mentioned 7 times since March 2021.

social mentions
7 vs 0
GPU Servers popularity
100% vs 0%
alternatives listed
42 vs 2

Base details

Website, pricing, platforms and company facts side by side.

Cloud GPU
Hinode
Website cloud.google.com hinode.run
Pricing —
Paid $0.29 (Workspace) Official pricing
Platforms —
Browser Windows MacOS Linux +1
Company — Startup from the United States · 1 - 9 employees · 2026
Listed in
Cloud Computing AI GPU Servers Billing & Invoicing +1
Cloud GPU Cloud Computing Generative AI AI Tools Cloud Desktop Virtual Desktop AI +4

About Cloud GPU and Hinode

In their own words, as submitted to SaaSHub.

Cloud GPU
Hinode

No description of Cloud GPU yet.

Hinode is a cloud workspace for people who make images and video with AI. The workspace keeps your files, installed applications and a shared folder for models and datasets between sessions. When a job needs a whole GPU, you attach a machine beside it: NVIDIA L4 with 24 GB VRAM, L40S with 48 GB...

Read more about Hinode

Features and specs

What each product offers, as listed by its team.

Cloud GPU 5 features
Hinode 13 features
  • Scalability
    Cloud GPUs offer scalable resources, allowing users to easily adjust the amount of GPU power they need depending on their workloads without investing in physical hardware.
  • Cost-Effectiveness
    Pay-as-you-go pricing models and the absence of upfront costs for hardware make cloud GPUs a cost-effective solution for organizations that require flexibility in processing power.
  • Accessibility
    Cloud GPUs provide remote access to powerful computational resources, enabling users to perform graphic-intensive tasks from any location with an internet connection.
  • Integration and Ecosystem
    Cloud GPUs integrate seamlessly with other cloud services within the Google Cloud ecosystem, enhancing productivity and operational efficiency.
  • Maintenance-Free
    By using cloud GPUs, users are relieved of the responsibility of maintaining and upgrading hardware, which is handled by the cloud provider.

Possible disadvantages

  • Latency
    Cloud-based solutions can sometimes suffer from latency issues, especially if the user is geographically distant from the data center.
  • Data Security and Privacy
    Using cloud-based GPUs involves transferring data to and from the cloud, which may raise concerns about data security and privacy depending on the sensitivity of the information.
  • Dependency on Internet Connection
    The performance and reliability of cloud GPUs are heavily dependent on a stable and fast internet connection.
  • Potential Costs for High Usage
    While flexible pricing is a benefit, costs can escalate quickly with extensive GPU usage, potentially becoming more expensive than maintaining on-premises hardware for prolonged workloads.
  • Learning Curve
    Adopting cloud GPUs requires technical knowledge and training, which may involve a learning curve for teams unfamiliar with cloud technologies.
  • GPUs
    NVIDIA L4 (24 GB), L40S (48 GB), RTX PRO 6000 (96 GB); one full card per machine
  • Machine sizes
    4 to 64 vCPU, 32 to 256 GB RAM
  • Pricing
    Pay as you go, billed by the minute: workspace $0.297/hr while running, GPU machines $1.75–$12.58/hr
  • Persistent workspace
    Files, installed apps and settings stay between sessions
  • Auto-pause
    Workspace and machines pause after 15 idle minutes; disk is kept
  • Shared folder
    One folder for models and datasets, visible to the workspace and every machine
  • Streamed desktop
    Ubuntu desktop at 1080p60 in the Hinode app (macOS, Windows, Linux) or in Chrome and Edge
  • SSH and CLI
    ssh, scp, rsync, git and VS Code Remote-SSH; hinode command-line tool
  • AI assistant access
    MCP connector for Claude and other assistants to manage workspaces, machines and jobs
  • One-click apps
    ComfyUI, PyTorch, JupyterLab, Ollama, vLLM, AI Toolkit, Axolotl, VS Code, Chrome
  • Regions
    US East and EU Frankfurt, same prices
  • Storage and traffic
    Disk $0.15/GB-month, shared folder $0.14/GB-month, egress $0.10/GB
  • Budget alerts
    Monthly budget with email at 80% and 100%

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Cloud GPU
Hinode
100% 100%
GPU Servers
0% 0%
60% 60%
Cloud GPU
40% 40%
100% 100%
Cloud Computing
0% 0%
0% 0%
Cloud Desktop
100% 100%

Questions & Answers

As answered by people managing Cloud GPU and Hinode.

Why should a person choose your product over its competitors?

Hinode's answer:

Choose Hinode if you lose time rebuilding your setup. On a typical GPU rental, stopping a pod means losing what you installed or paying for storage and re-attaching it by hand. On Hinode you pause and come back to the same desktop, files and apps. The machine is billed by the minute and the workspace costs $0.297/hr while it runs. If all you need is the cheapest hour on a card for a one-off batch job, a raw GPU rental will cost less. Hinode is for people who come back to the same work every day.

Which are the primary technologies used for building your product?

Hinode's answer:

Go for the orchestrator that starts, pauses and bills machines. TypeScript for the web app, the MCP servers and internal tools. NVIDIA L4, L40S and RTX PRO 6000 GPUs running Ubuntu with a GNOME desktop streamed at 1080p60. Stripe for per-minute billing, Terraform for infrastructure.

What makes your product unique?

Hinode's answer:

Your GPU environment stays put. The workspace holds your files, installed apps and a shared folder for models, and it pauses itself after 15 idle minutes. When you need a whole card, you attach an L4, L40S or RTX PRO 6000 machine beside it. That machine starts from your saved workspace, so ComfyUI, your custom nodes and your models are already there. You reach the same environment through a streamed desktop, SSH, the hinode CLI, VS Code or an AI assistant over MCP.

How would you describe the primary audience of your product?

Hinode's answer:

People who make images and video with open models: ComfyUI users, LoRA trainers, and developers building nodes and tools on top of diffusion models. Many of them work on a Mac or a laptop that can't run the models they use. They want a GPU machine that keeps their environment between sessions, without managing cloud infrastructure themselves.

What's the story behind your product?

Hinode's answer:

Hinode is built in Barcelona by a solo founder with twelve years of experience building software and infrastructure. The idea came from watching AI creators lose hours to the same chores: renting a pod, reinstalling ComfyUI, re-downloading models and losing it all when the pod stopped. Paperspace shut down its desktop product in 2024, and nothing replaced it for this audience. Hinode is our answer: a GPU workspace that is still there the next day. The name means sunrise in Japanese.

User comments

Share your experience with using Cloud GPU and Hinode. For example, how are they different and which one is better?

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Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Cloud GPU 7 mentions
Hinode 0 mentions
  • Does Google Cloud GPU use physical GPUS or are they emulated
    Per https://cloud.google.com/gpu, they use NVIDIA L4, P100, P4, T4, V100, and A100 GPUs. These are physical units loaded into servers and then shared to the OS by the hypervisor. Source: over 3 years ago
  • Fine-tuning?
    You probably can't do it through onedrive, though I'm not sure if MS has something like that that carries over into other services. The thing you need is GPU power, not storage. Most people use something like google cloud... Source: over 3 years ago
  • Home Server - Student
    Uh, you ask these questions before you buy the hardware. There are various tools you could have used for free, or for cheap instead of spending $2500 on equipment, and not even seemingly the right equipment. You would know more than me,... Source: almost 4 years ago

View more

Tracking Hinode since Sep 2026.

Alternatives to Cloud GPU and Hinode

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