TL;DR: Comfy Cloud is easier and gives you access to powerful hosted GPUs without buying hardware. Local ComfyUI gives you more control, unrestricted custom nodes, offline use and potentially lower long-term cost if you already own a capable GPU. The better choice depends on how often you generate, what models you use and how much environment control you need.
If you are choosing between Comfy Cloud and a local GPU, the decision is not simply cloud versus desktop. You are trading infrastructure convenience against control, recurring compute cost against hardware ownership, and a managed environment against the freedom to customize everything.
Comfy Cloud vs local ComfyUI at a glance
Comfy Cloud: best when you want to start immediately, do not have a strong GPU, work on a Mac or lightweight laptop, or need high-VRAM image and video workflows without maintaining a machine.
Local ComfyUI: best when you already own a capable NVIDIA GPU, need arbitrary custom nodes and models, want offline operation, or run enough jobs that recurring cloud usage becomes more expensive than using your own hardware.
1. Setup and maintenance
Comfy Cloud removes most of the infrastructure work. You open the interface in a browser, choose or build a workflow and run it on hosted hardware. There is no local CUDA stack to maintain, no Python environment to repair and no need to keep large checkpoints on your own disk.
Local ComfyUI takes more work. You need a compatible GPU backend, the correct PyTorch environment, enough storage for checkpoints and LoRAs, and a strategy for updating ComfyUI and custom nodes without breaking working pipelines. For the practical setup steps, see How to Install ComfyUI on Windows, macOS, and Linux.
2. GPU power and VRAM
This is where Cloud has an immediate advantage for users with weak hardware. Comfy currently runs its hosted service on Blackwell RTX 6000 Pro GPUs with 96 GB of VRAM. That is far beyond the VRAM available on most consumer laptops and many desktop GPUs.
For image generation, an 8-24 GB local GPU can still be very useful depending on the model, quantization and workflow. Video generation, large upscaling chains and multi-model workflows are much more demanding, so Cloud becomes more attractive when the graph no longer fits comfortably in local VRAM.
3. Cost: subscription vs hardware you already own
Comfy Cloud currently starts at $20 per month on the Standard plan. Creator is $35 per month and Pro is $100 per month, with lower effective monthly pricing on annual billing. Cloud usage is credit-based, and credits are consumed by active GPU runtime and Partner Nodes rather than by the time you spend editing a graph.
If you already own a suitable GPU, local inference has no per-generation Comfy charge. You still pay for electricity, storage, hardware depreciation and your own time maintaining the environment, but heavy users can find local execution more economical over time.
If you generate only occasionally, the opposite can be true: paying for managed access may be more rational than buying an expensive GPU that sits idle most of the month.
4. Models, LoRAs and custom nodes
Local ComfyUI gives you the broadest control. You can install arbitrary compatible custom nodes, keep experimental model builds, modify Python dependencies, use your own directory structure and patch the environment when a workflow requires something unusual.
Comfy Cloud provides a large managed model library and supports many widely used custom nodes. Creator, Pro and Team plans also support importing your own models or LoRAs from supported sources such as CivitAI and Hugging Face. The trade-off is that Cloud cannot expose every possible community dependency or experimental local setup.
5. Video generation
For video workflows, Cloud is often the more practical starting point. High frame counts, larger resolutions, multi-stage generation and upscaling can quickly exceed consumer VRAM. A managed 96 GB environment lets you test demanding workflows without first investing in workstation-class hardware.
Local still makes sense for repeated production when your hardware is strong enough and your workflow is stable. Once a graph fits reliably on your own machine, repeated local runs avoid cloud credit consumption. See ComfyUI Video Workflows for how video pipelines are structured.
6. Mac and low-power laptops
Cloud is usually the simpler option when your main computer is a Mac or a laptop without a dedicated NVIDIA GPU. You can still use the full node interface in the browser while the heavy inference happens remotely.
Local ComfyUI can run on Apple Silicon and other supported hardware, but large open-weight video models may be slow or impractical compared with a high-VRAM cloud GPU.
7. Privacy and offline work
Local ComfyUI has a structural privacy advantage: workflows and source assets can remain entirely on your machine, and you can run without an internet connection once the required models are installed.
Cloud requires uploading inputs needed by the workflow and running them on remote infrastructure. Comfy states that Cloud inputs, outputs and workflows are private to the account by default, but teams handling confidential client data should still evaluate their own security, contractual and data-handling requirements.
8. Automation, API and AI agents
Cloud becomes especially interesting when ComfyUI is part of a larger production system. Comfy offers API execution and a public-beta MCP connection that can let compatible agents discover models and templates, submit workflows and retrieve outputs.
Local ComfyUI can also be automated and now has an official local MCP server. This means the choice is not between automation and no automation: it is between automation on your own hardware and automation on managed cloud infrastructure.
Which option should you choose?
Use Comfy Cloud first if you are new to ComfyUI, do not have a suitable GPU, need high-VRAM video workflows, or want to test the ecosystem before buying hardware. New users currently get 5 free GPU runs, which is enough to test a small workflow before subscribing. For the hosted setup path, read our ComfyUI Cloud guide; for the broader workflow concepts, start with our ComfyUI guide.
Use local ComfyUI first if you already have a strong GPU, require unrestricted custom nodes, handle sensitive offline work, or expect to run the same workflows heavily and continuously.
A hybrid setup is also practical. You can build and run normal workloads locally, then move only the jobs that need more VRAM or a managed environment to the cloud. For a deeper explanation of the hosted option, read ComfyUI Cloud: How to Run ComfyUI Online Without a Local GPU.
Sources
Primary references: Comfy Cloud, Comfy pricing, and Comfy MCP documentation.
