Comfy Cloud vs ViewComfy: Which Comfy Hosting Platform Is Right for You?

ComfyUI has become a popular interface for building advanced image and video generation workflows, but its flexibility often comes with setup complexity and GPU requirements.

To solve this, several platforms now offer ComfyUI in the cloud. Two of the most notable options are Comfy Cloud and ViewComfy, both removing the need for local hardware, but optimized for very different use cases.

This post breaks down how Comfy Cloud vs ViewComfy compare across performance, pricing, flexibility, and production readiness, so you can choose the platform that best fits your workflow.

What Is Comfy Cloud?

Comfy Cloud is the official cloud offering for running ComfyUI with zero setup. It’s designed to get users from opening their browser to generating images in seconds.

The platform focuses on simplicity:

• No installation
• Preloaded models and node packs
• Automatic updates
• Shared access to powerful GPUs

For creators who want to explore ComfyUI quickly or avoid any configuration overhead, Comfy Cloud removes nearly all friction.

What Is ViewComfy?

ViewComfy is a ComfyUI hosting platform built for teams that need a flexible, scalable, and private environment to run workflows. If it runs locally, it can run on ViewComfy.

ViewComfy gives users:

• A private ComfyUI environment running on dedicated GPUs
• Full access to the ComfyUI Manager, including support for private custom nodes
• Private model storage that supports any model type
• The ability to turn workflows into apps or APIs in just a few clicks

Where Comfy Cloud optimizes for immediacy, ViewComfy optimizes for flexibility, scalability, and large teams that don't necessarily know ComfyUI.

TLDR: The difference largely comes down to Setup Time vs Flexibility

Platform	Time to First Image	Flexibility
Comfy Cloud	⚡ Seconds	Limited
ViewComfy	⏳ More setup	Very high

Comfy Cloud shines when your workflow already fits its environment.
ViewComfy shines when your workflow diverges from the standard or needs to run at scale.

Dedicated GPU vs Shared GPU Architecture

One of the most important differences between Comfy Cloud and ViewComfy is how GPU resources are allocated.

Comfy Cloud: Shared GPU Cluster

Comfy Cloud runs all users on a shared cluster of RTX 6000 GPUs.

Benefits

• No setup required
• Minimal cold-start time
• No infrastructure decisions required

Trade-offs

• Requests may be queued during peak traffic
• No option to choose faster GPUs

ViewComfy: One Workflow, One GPU Cluster

ViewComfy assigns a dedicated GPU cluster to each workflow, selectable from a range of GPU options.

Benefits

• Fully isolated performance
• Ability to customize autoscaling behavior to match usage patterns
• Predictable latency
• Option to run workflows on GPUs that best match the workload

Trade-offs

• Depending on autoscaling configuration, cold starts may occur
• While the infrastructure works out of the box, it requires configuration to handle sustained high load efficiently

Pricing: Credits vs Transparent GPU Seconds

Comfy Cloud Pricing

Comfy Cloud uses a credit-based system, where GPU usage consumes credits based on how many resources a workflow requires.

This is simple to get started with, but can make cost forecasting harder.

ViewComfy Pricing

ViewComfy uses per-GPU-second pricing, based on the GPU model in use. This makes it straightforward to forecast costs across workflows, users, and projects.

Custom Nodes, Dependencies, and Model Flexibility

Comfy Cloud

Comfy Cloud provides:

• A curated set of pre-installed custom nodes
• Preloaded popular models
• LoRA imports from CivitAI on select plans

However:

• You cannot install node packs that are not already available
• Model availability is limited to what’s preloaded (outside of LoRAs)

This keeps the platform stable, but limits flexibility for advanced or highly customized workflows.

ViewComfy

ViewComfy exposes the full ComfyUI Manager, just like a local setup:

• Install any public or private custom node
• Pull nodes from private GitHub repositories
• Install system and Python dependencies
• Import any model into permanent storage

The main drawback of this approach is that the workflow needs to be set up before it can be used for the first time.

From Workflows to Apps and APIs

This is where the two platforms diverge most clearly.

Comfy Cloud: Interactive Creation

Comfy Cloud is optimized for usage inside the ComfyUI interface. It’s ideal for solo creators or teams already comfortable with ComfyUI who simply need a place to run their workflows online.

ViewComfy: Production Deployment

ViewComfy is designed to take workflows beyond ComfyUI:

• Turn workflows into web apps that non-technical users can use
• Organize and share apps internally via your own App Hub
• Deploy workflows as autoscaling APIs
• Integrate easily with existing products and pipelines

This makes ViewComfy well suited for design teams where not everyone knows ComfyUI, as well as for teams building products that require a scalable API.

Team Management and Observability

Comfy Cloud

• Simple onboarding
• Limited team controls
• Primarily focused on individual usage

ViewComfy

• User and team management
• Usage dashboards
• Cost tracking and visibility

Which Platform Should You Choose?

Choose Comfy Cloud if you:

• Want instant access to ComfyUI
• Prefer zero configuration
• Mostly use standard workflows
• Don’t need custom infrastructure

Choose ViewComfy if you:

• Need dedicated GPUs
• Install custom or private nodes
• Require full control over your model library
• Deploy workflows as apps or APIs
• Manage teams and costs at scale

Final Thoughts

Both platforms are excellent—but they solve different problems.

Comfy Cloud lowers the barrier to entry for ComfyUI. ViewComfy turns ComfyUI into production infrastructure.

If you’re experimenting, Comfy Cloud is hard to beat. If you’re building tools others rely on, ViewComfy is built for that reality.

Want to see how your existing workflow runs on a dedicated GPU? Try ViewComfy and turn your ComfyUI workflows into apps or APIs.