Turn ComfyUI workflows into production APIs

Export your ComfyUI workflow as JSON and Beam serves it as an API endpoint on cloud GPUs.

Load CheckpointCLIP EncodeCLIP EncodeKSamplerVAE DecodeSave Imageoutput.png

ComfyUI in production

Run Comfy on cloud GPUs without managing your own infrastructure.

Workflow JSON in, image out
Export workflow_api.json from the ComfyUI menu and host the same workflow behind a REST API.
Explore docs

Custom nodes supported
Install any node pack with comfy node install while building your image. What runs locally runs on Beam.
Explore docs

The full UI, hosted in minutes
Spin up interactive ComfyUI on a managed GPU Pod and open it in your browser.
Explore docs

Autoscaling included
Beam scales API containers with request volume.
Explore docs

Hosted outputs
Save generated images and get back public URLs you can return straight to your users.
Explore docs

Flux, SDXL, or anything
Point at any checkpoint on Hugging Face — Flux1 Schnell, SDXL, SD 1.5 — and swap models by changing a variable.
Explore docs

How it works

From workflow to API in minutes

01

Build ComfyUI into an image

Install ComfyUI with comfy-cli, add your custom nodes, and bake model weights into the image or a volume.

02

Export your workflow as JSON

Use ComfyUI's export menu to save workflow_api.json.

03

Deploy it as an API

Wrap the workflow in an endpoint, deploy, and generate images over HTTP.

# Wrap an exported workflow in an API (api.py)
workflow = json.loads(WORKFLOW_FILE.read_text())
workflow["6"]["inputs"]["text"] = item["prompt"]

subprocess.run(
    f"comfy run --workflow {workflow_file} --wait",
    shell=True, check=True,
)

output = Output(path=latest_image)
output.save()
return {"output_url": output.public_url()}

# Deploy it
$ beam deploy api.py:handler

# Generate images over HTTP
$ curl -X POST https://comfy-xyz.app.beam.cloud/generate \
    -H 'Authorization: Bearer YOUR_TOKEN' \
    -d '{"prompt": "A cat in a spacesuit"}'

Frequently asked questions

Can I use custom nodes?
Yes. Install node packs with comfy node install as part of your image build — the same nodes you use locally work in the cloud.

How do model weights get loaded?
Bake them into the container image with huggingface-cli download, or store them on a persistent volume shared across containers.

Can I run the ComfyUI interface itself?
Yes. A Pod hosts the full web UI behind an SSL-terminated URL, backed by whatever GPU you pick.

Which GPU do I need for my model?
SD 1.5 and SDXL run comfortably on 24Gi cards like the A10G or RTX 4090. Flux-scale checkpoints want an H100 with 80Gi.

How fast does a workflow start generating?
Once your image is built, containers boot in seconds with weights already cached — there's no template to initialize and no models to install by hand through the UI.