If you want to experiment with ComfyUI but don’t want to invest in an expensive GPU, you still have options thanks to various cloud providers. However, you’ll need to carefully consider the trade-offs. Here’s what I’ve learned from my own experience (and some research with GPT):

1. Google Colab

Google Colab was my initial go-to for quick experiments. It provides free access to GPUs through Jupyter notebooks, so you can get ComfyUI running quickly by following the official instructions. The downside? Sessions are time-limited and can disconnect after a few hours, and free GPUs aren’t always available. Customizing the environment can also be tricky since everything resets when your session ends. Still, if you want to try ComfyUI without entering your credit card, Colab is a solid starting point.

2. RunComfy

I recently tried out RunComfy, and it’s by far the easiest way to use ComfyUI in the cloud. There’s no installation or setup—just sign up and you’re greeted with a slick, node-based interface right in your browser. What really stands out is the huge range of models it supports, from SDXL and SD3 to AnimateDiff and video workflows. You don’t need any coding experience to build complex pipelines; it’s all drag-and-drop and visually intuitive. Of course, this convenience isn’t free, and if you’re a power user who wants to tinker with every detail or run custom environments, you might find some limitations. But for most people, especially if you want to avoid the hassle of managing your own GPU server, RunComfy is a fantastic option that lets you focus on creativity instead of infrastructure.0

3. RunPod

If you want full control and serious GPU power, RunPod is worth a look. It lets you rent high-end GPUs on demand, and you can either use ready-made ComfyUI templates or set up your own custom environment. I like that you get persistent storage for your models and outputs, and you can install any custom nodes or dependencies you want. The downside is there’s no free tier—everything is billed hourly, and the setup can be a bit more involved than Colab or RunComfyUI. But if you’re running production workloads, need persistent storage, or want to customize everything, RunPod is a strong choice.

RunPod Deep Dive

Let’s take a closer look at RunPod, since it involves a few more steps but offers the most flexibility. Here’s how I run ComfyUI on RunPod with persistent storage:

Step-by-step: Running ComfyUI on RunPod with Persistent Storage

1. Sign up and log in to RunPod
   Go to runpod.io and create an account if you don’t have one.

2. Create a Network Volume

   • In the sidebar, go to “Storage” and click “New Network Volume”.
   • Choose a data center close to you, give your volume a name, and select the storage size (10GB+ is recommended if you plan to use custom models or nodes).
   • This network volume will persist your models, outputs, and custom nodes even after your pod is stopped or deleted.

3. Deploy a Pod with the ComfyUI Template

   • Go to “GPU Cloud” and click “Deploy Pod”.
   • Under “Templates”, search for and select the “ComfyUI” template.
   • Attach your network volume to the pod during setup.
   • Choose your GPU type (NVIDIA A10G, 3090, 4090, etc.) and set the number of GPUs (usually 1 is enough for most workflows).
   • For best value, you can select “Spot” pricing.

4. Access ComfyUI

   • Once the pod is running, open the provided web URL (usually port 8188) to access the ComfyUI interface in your browser.
   • You can now upload models, custom nodes, and save outputs directly to your network volume.

5. Install Custom Nodes or Models (Optional)

   • I recommend using the ComfyUI Manager to install custom nodes and models.
   • If you can’t find what you need through the manager, use the pod’s web terminal to clone custom nodes into /workspace/custom_nodes or upload models to /workspace/models.
   • These will persist across sessions thanks to the network volume.

6. Shut Down When Done

   • Stop your pod when you’re finished to avoid unnecessary charges. Your data will remain safe in the network volume for next time.
   • Optionally, you can start a new pod with your network volume attached and verify your custom nodes and models are still there.

Cost Estimation

• Secure Cloud: Dedicated, higher reliability, slightly higher price.

• Community Cloud: Cheaper, but your session may be interrupted if resources are needed elsewhere (similar to “spot” pricing).

• Example: Running a ComfyUI pod with an RTX 4090 on Community Cloud for 2 hours, with a 20GB network volume for a month:
  2 x $0.34 + 20 x $0.10 = $0.68 + $2.00 = $2.68

vs RunComfy:

Example: 4090 Cost Comparison (2 hours/day for 1 month, 20GB storage)

• RunPod: GPU price per hour, plus $0.10/GB/month for storage. For 60 hours and 20GB storage: Community = $22.40, Secure = $43.40.
• RunComfy: $1.39/hr for Large Machine (Pro), storage included. For 60 hours: $83.40.

For users planning extended ComfyUI usage, RunPod offers substantial cost savings despite requiring some initial setup work. In my experience, the configuration process was more straightforward than anticipated. However either way it’s much cheaper than you buy a 4090+ yourself unless you admit you really just want to use it for gaming:)