Comfy is the open-source ComfyUI engine and its official cloud: build image, video, 3D and audio pipelines by connecting nodes on a canvas, run them on your own hardware for free, or on managed GPUs when you need scale.
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Comfy is the AI creation engine for visual professionals who want control over every model, every parameter and every output. In practice it is two things at once: ComfyUI, a long-running open-source project that puts a node graph in front of diffusion models, and a set of commercial services built around that engine by the team at comfy.org — Desktop, Cloud, API and Enterprise. Understanding this dual structure is the key to understanding the product, because the free thing and the paid thing are genuinely different offerings rather than tiers of one subscription.
The engine is not new or obscure. The repository carries roughly 128,000 stars and more than 15,000 forks, and was still receiving commits on the day this was written, having started in January 2023. Its own description is a modular diffusion model GUI, API and backend with a graph and nodes interface, which is a more honest summary than most marketing copy: it is a graph editor for generative pipelines rather than a prompt box with buttons.
Where most consumer image tools hide the pipeline, Comfy exposes it. Models, processing steps and outputs are connected on an infinite canvas where every decision is visible and every step is inspectable. That is the whole proposition. If you want to type a prompt and receive a picture, this is the wrong tool and a simpler one will serve you better. If you need to know exactly which sampler ran at which step count with which LoRA applied at which weight — and to reproduce that result a thousand times — this is close to the only tool that shows you.
The local path is free in a way that deserves emphasis. Comfy Desktop is free forever, with no feature gates, no trial periods and no pro tier for core functionality, and the official framing adds that no vendor can lock you in or force you off the platform. The engine is open source under GPL-3.0, so this is a structural guarantee rather than a promise that could be withdrawn in a future pricing change. The cost of the local path is hardware: you need a capable GPU, and that cost is real even though it is not a subscription.
Comfy Cloud is the commercial side, and it is a subscription with a credit allowance rather than a flat-rate service. Comfy Cloud starts with five free runs on real GPUs and no credit card, then moves to Standard at 20 dollars a month, with Creator at 35 and Pro at 100, each discounted by roughly twenty percent when billed annually, and each carrying a progressively larger monthly credit pool. A Team plan and a custom Enterprise tier sit above these. Crucially for cost control, you are charged only for active GPU time while a workflow runs, and the time spent building a workflow does not consume credits — which means the exploratory phase is nearly free even on Cloud. Runs are capped at 30 minutes, extended to one hour on Pro, with over-running jobs cancelled automatically. Additional credits can be purchased at any time, and partner nodes draw from the same balance. Prices here reflect the official pricing page at the time of writing and should be confirmed there, as tiers and credit allowances change.
The engine is free and open source, and Comfy Desktop runs it on your own hardware with no feature gates, no trial period and no pro tier for core functionality. What costs money is managed infrastructure: Comfy Cloud provides GPUs by subscription with a monthly credit pool, the API is billed through the same credits, and Enterprise is priced by arrangement. You never have to pay to use the core software; you pay when you want someone else's hardware.
GPL-3.0 — the GNU General Public License version 3, confirmed from the repository's own licence file. This matters because GPL-3.0 is a strong copyleft licence, not the permissive MIT licence that is frequently and incorrectly attributed to it. Using the software is unrestricted; distributing modified versions carries obligations. If you intend to build a commercial product on the engine, get legal advice specific to your plans.
For local use, yes, and this is the single most common practical obstacle. VRAM is usually the binding constraint rather than raw speed, and memory exhaustion is by a wide margin the most reported problem in the project's issue tracker. Video models are especially demanding. If your hardware falls short, Comfy Cloud runs the same workflows on 96GB cards, which is the intended answer to this limitation.
Steep. The node graph is the product, and there is no version of using it well that avoids learning how the pipeline fits together. App Mode lets you run someone else's workflow through a simplified interface on day one, which is a genuine on-ramp, but building or meaningfully modifying pipelines requires real investment. If you want good images with no learning curve, choose a different tool — that is a fair recommendation rather than a criticism.
On Comfy Cloud, the vendor states every available model is cleared for commercial use with no licence ambiguity. Running locally, the answer depends entirely on the licence of each model you have downloaded, and those vary considerably — some permit commercial use freely, others restrict it to research. The engine's own licence does not govern this. Check each checkpoint individually.
Yes, and this is a deliberate design point rather than a side effect. Comfy Desktop requires no internet connection after setup, so workflows, models and data stay on your machine. This is the main reason studios under NDA and users in regulated environments choose the local build over any hosted alternative.
Images, video, 3D and audio, though not in equal measure. Image generation is the deepest and most mature capability, video is now a first-class use case with recent open and partner models well supported, and 3D and audio are present but less central. Open-weight models including Wan, Flux, LTX and Qwen Image Edit are supported alongside partner models available on Cloud.
Build and test the workflow in the interface, export it in API format, obtain an API key, and submit the workflow JSON from your own code, injecting whatever inputs vary per call. The typical shape is generation sitting between two other steps you control — fetching an input from storage beforehand and writing the result somewhere afterwards.
Yes, by any reasonable measure. The repository shows commits on the day of writing, roughly 128,000 stars and over 15,000 forks since its creation in early 2023, and new models are implemented as they launch. The large open issue count reflects the size of the user base and the breadth of hardware configurations rather than neglect, though it does mean individual reports may go unaddressed for a long time.
Run locally if you have a capable GPU, care about offline operation or confidentiality, or expect heavy exploratory use where per-second billing would add up. Use Cloud if your hardware is insufficient, you need more VRAM than consumer cards provide, you want models and popular custom nodes pre-installed, or you need the commercial-licence assurance. Many users do both: iterate locally, then run volume jobs on managed hardware.
