🔒 Auto-Scaling ComfyUI-API and ComfyUI: Orchestrating GPU Workloads with Azure Kubernetes Service and KEDA

Introduction I am currently working on a project that relies heavily on multiple AI features built on Stable Diffusion models. The AI team uses ComfyUI and ComfyUI-api as the workflow orchestrator for Stable Diffusion. This allows them…

Introduction

I am currently working on a project that relies heavily on multiple AI features built on Stable Diffusion models. The AI team uses ComfyUI and ComfyUI-api as the workflow orchestrator for Stable Diffusion. This allows them to design and optimise workflows visually in ComfyUI, then export each AI feature as a workflow definition (in JSON format), for example:

https://github.com/SaladTechnologies/comfyui-api/blob/main/example-workflows/sd1.5/txt2img.json

All model training and fine-tuning work (including LLM and VLM models) is currently performed on GPU-enabled virtual machines.

After joining the team as a Solutions Architect, I worked closely with the backend engineers to refactor large parts of the application layer. This included applying the Asynchronous Request–Reply pattern, agent workflow orchestration patterns, webhooks, Azure Blob Storage integration, and other architectural improvements. As a result, the application tier now scales significantly better.

However, a major bottleneck remained: all AI workloads were still running on a single GPU VM, exposed via docker-compose up. The same machine was used for training, fine-tuning, and inference, creating tight coupling, poor isolation, and limited scalability.

Since this domain was relatively new to me, I spent time surveying the ecosystem for tools that could support scalable inference. I evaluated Ollama, Foundry Local, vLLM, KServe, and KAITO. In practice, none of these solutions fit the requirement, because ComfyUI and ComfyUI-api are not simple LLM inference endpoints—they are a workflow-based Stable Diffusion orchestrator with their own API server.

In the end, I settled on a straightforward, production-ready solution: Azure Kubernetes Service (AKS) with KEDA enabled. I spent multiple nights provisioning, deploying, validating, and stress-testing each component.

This blog consolidates that deployment work. I will walk through:

Provisioning an AKS cluster with GPU nodes (Standard_NC4as_T4_v3)

Containerising ComfyUI and ComfyUI-api, including workflow and model downloads

Enabling KEDA-based autoscaling with HTTP add-on

Demonstrating the final results

Let’s get started.

Build the comfyui-api execution file

My working environment is Windows 11 + WSL2 with Ubuntu 24

First of all, you need to check out the comfyui-api to build the execution file.

git clone https://github.com/SaladTechnologies/comfyui-api.git

cd comfyui-api

npm install

npm run build-binary

Dockerize the comfyui and comfyui-api app

Package the comfyui and comfyui-api as well as copy all workflows and pull all LLM + VLM models (in this case, dreamshaper5.safetensors, dreamshaper_8.safetensors, and Qwen 3 VL) to the Dockerfile.

# Custom ComfyUI API image with QwenVL support

# Base image: comfyui-api with ComfyUI 0.7.0, API 1.16.1, PyTorch 2.8.0, CUDA 12.8

FROM ghcr.io/saladtechnologies/comfyui-api:comfy0.7.0-api1.16.1-torch2.8.0-cuda12.8-runtime



# Set environment variables

ENV WORKFLOW_DIR=/workflows

ENV STARTUP_CHECK_MAX_TRIES=30



# Install ComfyUI-QwenVL custom node

WORKDIR /opt/ComfyUI/custom_nodes

RUN git clone https://github.com/1038lab/ComfyUI-QwenVL && \

    pip install --no-cache-dir -r ComfyUI-QwenVL/requirements.txt



# Optional: Install llama-cpp-python for GGUF support (uncomment if needed)

# RUN pip install --no-cache-dir llama-cpp-python



# Copy workflows into the image

COPY example-workflows/sd1.5 /workflows



# Copy the comfyui-api binary

COPY bin/comfyui-api /app/comfyui-api

RUN chmod +x /app/comfyui-api



# Copy the model download script (downloads models at runtime)

COPY docker/download-models.sh /app/download-models.sh

RUN chmod +x /app/download-models.sh



# Set working directory

WORKDIR /app



# Run the entrypoint script (downloads models then starts API)

CMD ["/app/download-models.sh"]

And download-models.sh:

#!/bin/bash

set -e



MODELS_DIR="/opt/ComfyUI/models/checkpoints"

mkdir -p "$MODELS_DIR"



# Download dreamshaper_8 if not exist

if [ ! -f "$MODELS_DIR/dreamshaper_8.safetensors" ]; then

  echo "Downloading dreamshaper_8.safetensors..."

  wget -q --show-progress -O "$MODELS_DIR/dreamshaper_8.safetensors" \

    "https://civitai.com/api/download/models/128713?type=Model&format=SafeTensor&size=pruned&fp=fp16"

  echo "✓ dreamshaper_8.safetensors downloaded"

else

  echo "✓ dreamshaper_8.safetensors already exists, skipping download"

fi



# Download dreamshaper5 if not exist

if [ ! -f "$MODELS_DIR/dreamshaper5.safetensors" ]; then

  echo "Downloading dreamshaper5.safetensors..."

  wget -q --show-progress -O "$MODELS_DIR/dreamshaper5.safetensors" \

    "https://huggingface.co/Lykon/DreamShaper/resolve/main/DreamShaper_5_beta2_noVae_half_pruned.safetensors?download=true"

  echo "✓ dreamshaper5.safetensors downloaded"

else

  echo "✓ dreamshaper5.safetensors already exists, skipping download"

fi



echo "All models ready. Starting comfyui-api..."



# Start the API server

exec /app/comfyui-api

The reason we use CMD ["/app/download-models.sh"] because we don't want to pull all the big models into a Docker image, which makes the image really big and start-up time is slow sequentially.

Now you go to GitHub developer settings to create a personal access token.

Then, you can build and push the Docker image to a GitHub artefact like

docker login ghcr.io -u thangchung # it will ask for the GitHub access token just created

docker build -t ghcr.io/thangchung/agent-engineering-experiment/comfyui-api:qwenvl-1 -f docker/qwenvl.dockerfile .

docker push ghcr.io/thangchung/agent-engineering-experiment/comfyui-api:qwenvl-1

See the file I have pushed before at https://github.com/thangchung/agent-engineering-experiment/pkgs/container/agent-engineering-experiment%2Fcomfyui-api/650944015?tag=qwenvl-1

Setup AKS cluster with GPU in-place

We follow the guidance at https://learn.microsoft.com/en-us/azure/aks/use-nvidia-gpu?tabs=add-ubuntu-gpu-node-pool#manually-install-the-nvidia-device-plugin to set up the AKS cluster (remember we need to have a node pool with a GPU, which will run the AI workload, and another node pool is the normal one with CPU, and we will use this normal node pool to run the rest of the workloads).

And the node pool with GPU (because we are experimenting, so we use Standard_NC4as_T4_v3, in reality, we might need to consider the bigger one, and we also need to request Microsoft Azure supporting team for more quota to be able to create this GPU option):

Run comfyui and comfyui-api with normal workload on AKS

After you finish provisioning the AKS cluster, just run

kubectl apply -f - <<EOF

apiVersion: v1

kind: Namespace

metadata:

  name: comfyui

---

apiVersion: apps/v1

kind: Deployment

metadata:

  name: comfyui-api

  namespace: comfyui

  labels:

    app: comfyui-api

spec:

  replicas: 1

  selector:

    matchLabels:

      app: comfyui-api

  template:

    metadata:

      labels:

        app: comfyui-api

    spec:

      containers:

        - name: comfyui-api

          # GitHub Container Registry image (includes download-models.sh entrypoint)

          image: ghcr.io/thangchung/agent-engineering-experiment/comfyui-api:qwenvl-latest

          imagePullPolicy: Always

          ports:

            - name: api

              containerPort: 3000

              protocol: TCP

            - name: comfyui

              containerPort: 8188

              protocol: TCP

          env:

            - name: LOG_LEVEL

              value: "debug"

            - name: WORKFLOW_DIR

              value: "/workflows"

            - name: STARTUP_CHECK_MAX_TRIES

              value: "30"

          resources:

            limits:

              nvidia.com/gpu: 1

              memory: "16Gi"

              cpu: "4"

            requests:

              nvidia.com/gpu: 1

              memory: "8Gi"

              cpu: "2"

          livenessProbe:

            httpGet:

              path: /health

              port: 3000

            initialDelaySeconds: 120

            periodSeconds: 30

            timeoutSeconds: 10

            failureThreshold: 3

          readinessProbe:

            httpGet:

              path: /health

              port: 3000

            initialDelaySeconds: 90

            periodSeconds: 10

            timeoutSeconds: 5

            failureThreshold: 3

      # GPU node pool tolerations

      tolerations:

        - key: "sku"

          operator: "Equal"

          value: "gpu"

          effect: "NoSchedule"

      # Schedule on GPU nodes

      nodeSelector:

        accelerator: nvidia

      restartPolicy: Always

---

apiVersion: v1

kind: Service

metadata:

  name: comfyui-api-service

  namespace: comfyui

  labels:

    app: comfyui-api

spec:

  type: LoadBalancer

  ports:

    - name: api

      port: 3000

      targetPort: 3000

      protocol: TCP

    - name: comfyui

      port: 8188

      targetPort: 8188

      protocol: TCP

  selector:

    app: comfyui-api

EOF

Now you can port-forward the comfyui-api POD and do some curl to test it, but wait a minute, we will do the test when we have auto-scaling in place. Now moving on.

Run autoscaling comfyui and comfyui-api on AKS and KEDA

But with the really expensive GPU workload, we need to think about how we can save the cost if no one uses it. So we can set up the KEDA to auto scale to zero incase no traffic usage.

To be able to do auto scaling, we need to install KEDA:

helm repo add kedacore https://kedacore.github.io/charts

helm repo update

helm install keda kedacore/keda --namespace keda --create-namespace

Enable KEDA HTTP add-on (because we are based on the HTTP traffic to scale it up and down):

helm install http-add-on kedacore/keda-add-ons-http --namespace keda

Finally, we can run the app with KEDA enabled:

kubectl apply -f - <<EOF

apiVersion: v1

kind: Namespace

metadata:

  name: comfyui

---

apiVersion: apps/v1

kind: Deployment

metadata:

  name: comfyui-api

  namespace: comfyui

  labels:

    app: comfyui-api

spec:

  replicas: 0  # KEDA controls replicas

  selector:

    matchLabels:

      app: comfyui-api

  template:

    metadata:

      labels:

        app: comfyui-api

    spec:

      containers:

        - name: comfyui-api

          # GitHub Container Registry image (includes download-models.sh entrypoint)

          image: ghcr.io/thangchung/agent-engineering-experiment/comfyui-api:qwenvl-1

          imagePullPolicy: Always

          ports:

            - name: api

              containerPort: 3000

              protocol: TCP

            - name: comfyui

              containerPort: 8188

              protocol: TCP

          env:

            - name: LOG_LEVEL

              value: "debug"

            - name: WORKFLOW_DIR

              value: "/workflows"

            - name: STARTUP_CHECK_MAX_TRIES

              value: "30"

          resources:

            limits:

              nvidia.com/gpu: 1

              memory: "16Gi"

              cpu: "4"

            requests:

              nvidia.com/gpu: 1

              memory: "8Gi"

              cpu: "2"

          readinessProbe:

            httpGet:

              path: /health

              port: 3000

            initialDelaySeconds: 90

            periodSeconds: 10

            timeoutSeconds: 5

            failureThreshold: 3

      # GPU node pool tolerations

      tolerations:

        - key: "sku"

          operator: "Equal"

          value: "gpu"

          effect: "NoSchedule"

      # Schedule on GPU nodes

      nodeSelector:

        accelerator: nvidia

      restartPolicy: Always

---

# Internal service for KEDA HTTP Add-on to route traffic to

apiVersion: v1

kind: Service

metadata:

  name: comfyui-api-service

  namespace: comfyui

  labels:

    app: comfyui-api

spec:

  type: ClusterIP

  ports:

    - name: api

      port: 3000

      targetPort: 3000

      protocol: TCP

    - name: comfyui

      port: 8188

      targetPort: 8188

      protocol: TCP

  selector:

    app: comfyui-api

---

# KEDA HTTP Add-on - scales based on incoming HTTP requests

# Traffic flows: Client -> KEDA Interceptor -> comfyui-api-service -> Pod

apiVersion: http.keda.sh/v1alpha1

kind: HTTPScaledObject

metadata:

  name: comfyui-api-scaler

  namespace: comfyui

spec:

  hosts:

    - comfyui.local  # Use this hostname in requests (Host header)

  targetPendingRequests: 1   # Scale up on any pending request

  scaledownPeriod: 300       # 5 minutes idle before scale to zero

  scaleTargetRef:

    name: comfyui-api

    kind: Deployment

    apiVersion: apps/v1

    service: comfyui-api-service

    port: 3000

  replicas:

    min: 0   # Scale to ZERO when idle

    max: 2   # Maximum replicas

EOF

In this case, if after 5 minutes there is no HTTP traffic, then KEDA will scale it down to zero automatically.

Make it a test

We need to do a port-forward for the KEDA HTTP interceptor proxy as

kubectl port-forward svc/keda-add-ons-http-interceptor-proxy -n keda 3000:8080

Now try to curl as below

curl -H "Host: comfyui.local" http://localhost:3000/health

You will got

But when you go back to the GPU's node pool, you will see it provisioning automatically.

And the POD will be running (in pending status):

Waiting around 2 minutes, then the POD will be up and running:

Now you should see:

If you go to http://localhost:3000/docs, you should see the API document of the comfyui-api:

If you notice in the red box, you would see 3 workflows that we deploy: img2img, qwen3-vl3-experiment, and txt2img

Now, let test the txt2img worklow with a payload like:

{

  "id": "7f350df6-49a9-4cd0-88de-5b53df870003",

  "webhook_v2": "https://webhook.site/b59a7434-c944-4897-91b8-5cd808219094",

  "input": {

    "prompt": "Create a photorealistic image of a woman standing outdoors on what appears to be a sunny autumn day. She has shoulder-length black hair and is wearing a Vietnamese Ao Dai. The background features blurred trees in the Tet holiday season. The lighting suggests natural sunlight, casting soft shadows that highlight her figure. The scene includes other people in casual attire, suggesting a public or social setting. The camera angle is at eye level, focusing on the woman's upper body and face while slightly capturing the background to provide context. The whole picture in Ho Chi Minh City",

    "negative_prompt": "",

    "checkpoint": "dreamshaper_8.safetensors"

  }

}

And do a curl request:

curl -X POST http://localhost:3000/workflow/txt2img \

  -H "Content-Type: application/json" \

  -H "Host: comfyui.local" \

  -d @payload.json

After a minute, I go to https://webhook.site/#!/view/b59a7434-c944-4897-91b8-5cd808219094/bfab5db6-c8e9-463e-8936-9bfe3dd4d289/1, copy the base64 picture there and paste it to base64-to-image-converter:

But after 5 minutes, if there is no HTTP traffic, then the POD is gone:

Conclusion

This brings me to the end of my experiment: scaling ComfyUI and ComfyUI-api on AKS using KEDA. From my perspective, the result is both practical and satisfying.

If you’ve read through this post and see opportunities for improvement or have alternative approaches to scaling a workflow-based Stable Diffusion orchestrator like ComfyUI. I’d be interested to hear your thoughts in the comments.

Happy hacking.

Appendix - comfyui and comfyui-api logs

✓ dreamshaper5.safetensors downloaded

All models ready. Starting comfyui-api...

{"level":20,"time":1769010999301,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","file":"/workflows/img2img.js","msg":"Evaluating workflow file"}

{"level":40,"time":1769010999370,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","module":"RemoteStorageManager","error":{},"msg":"Error initializing storage provider S3StorageProvider"}

{"level":30,"time":1769010999302,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","workflow":"img2img","file":"/workflows/img2img.js","msg":"Loaded workflow"}

{"level":20,"time":1769010999302,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","file":"/workflows/img2img.ts","msg":"Transpiling TypeScript workflow"}

{"level":20,"time":1769010999332,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","file":"/workflows/img2img.js","msg":"Evaluating workflow file"}

{"level":30,"time":1769010999333,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","workflow":"img2img","file":"/workflows/img2img.js","msg":"Loaded workflow"}

{"level":20,"time":1769010999333,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","file":"/workflows/qwen3-vl-experiment.js","msg":"Evaluating workflow file"}

{"level":30,"time":1769010999334,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","workflow":"qwen3-vl-experiment","file":"/workflows/qwen3-vl-experiment.js","msg":"Loaded workflow"}

{"level":20,"time":1769010999334,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","file":"/workflows/qwen3-vl-experiment.ts","msg":"Transpiling TypeScript workflow"}

{"level":20,"time":1769010999353,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","file":"/workflows/qwen3-vl-experiment.js","msg":"Evaluating workflow file"}

{"level":30,"time":1769010999353,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","workflow":"qwen3-vl-experiment","file":"/workflows/qwen3-vl-experiment.js","msg":"Loaded workflow"}

{"level":20,"time":1769010999354,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","file":"/workflows/txt2img.js","msg":"Evaluating workflow file"}

{"level":30,"time":1769010999354,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","workflow":"txt2img","file":"/workflows/txt2img.js","msg":"Loaded workflow"}

{"level":20,"time":1769010999354,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","file":"/workflows/txt2img.ts","msg":"Transpiling TypeScript workflow"}

{"level":20,"time":1769010999367,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","file":"/workflows/txt2img.js","msg":"Evaluating workflow file"}

{"level":30,"time":1769010999367,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","workflow":"txt2img","file":"/workflows/txt2img.js","msg":"Loaded workflow"}

{"level":40,"time":1769010999370,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","module":"RemoteStorageManager","error":{},"msg":"Error initializing storage provider AzureBlobStorageProvider"}

{"level":30,"time":1769010999370,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","module":"RemoteStorageManager","msg":"Initialized with 2 storage providers"}

{"level":30,"time":1769010999371,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","module":"RemoteStorageManager","msg":"Cache populated with 0 files, total size: 0.00 B"}

{"level":30,"time":1769010999371,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","msg":"Starting ComfyUI API 1.16.1 with ComfyUI 0.7.0"}

2082600K .......... .......... .......... .......... ..        100%  218M=9.4s2026-01-21 15:56:39 - root - DEBUG - Tracking command: launch with arguments: {'extra': ['--listen', '*'], 'background': False, 'frontend_pr': None}

2026-01-21 15:56:39 - root - DEBUG - tracking event called with event_name: launch and properties: {'extra': ['--listen', '*'], 'background': False, 'frontend_pr': None}

╭──────────────────────────── 🔔 Update Available! ────────────────────────────╮

│ ✨ Newer version of comfy-cli is available: 1.5.4.                           │

│ Current version: 1.5.3                                                       │

│ Update by running: 'pip install --upgrade comfy-cli' ⬆                       │

╰──────────────────────────────────────────────────────────────────────────────╯



Launching ComfyUI from: /opt/ComfyUI



[START] Security scan

[DONE] Security scan

## ComfyUI-Manager: installing dependencies done.

** ComfyUI startup time: 2026-01-21 15:56:40.917

** Platform: Linux

** Python version: 3.11.13 | packaged by conda-forge | (main, Jun  4 2025, 14:48:23) [GCC 13.3.0]

** Python executable: /opt/conda/bin/python

** ComfyUI Path: /opt/ComfyUI

** ComfyUI Base Folder Path: /opt/ComfyUI

** User directory: /opt/ComfyUI/user

** ComfyUI-Manager config path: /opt/ComfyUI/user/__manager/config.ini

** Log path: /opt/ComfyUI/user/comfyui.log



Prestartup times for custom nodes:

   1.7 seconds: /opt/ComfyUI/custom_nodes/ComfyUI-Manager



Checkpoint files will always be loaded safely.

Total VRAM 15931 MB, total RAM 28063 MB

pytorch version: 2.8.0+cu128

Set vram state to: NORMAL_VRAM

Device: cuda:0 Tesla T4 : cudaMallocAsync

Using async weight offloading with 2 streams

Enabled pinned memory 26659.0

Using pytorch attention

Python version: 3.11.13 | packaged by conda-forge | (main, Jun  4 2025, 14:48:23) [GCC 13.3.0]

ComfyUI version: 0.7.0

ComfyUI frontend version: 1.35.9

[Prompt Server] web root: /opt/conda/lib/python3.11/site-packages/comfyui_frontend_package/static

Total VRAM 15931 MB, total RAM 28063 MB

pytorch version: 2.8.0+cu128

Set vram state to: NORMAL_VRAM

Device: cuda:0 Tesla T4 : cudaMallocAsync

Using async weight offloading with 2 streams

Enabled pinned memory 26659.0

### Loading: ComfyUI-Manager (V3.39)

[ComfyUI-Manager] network_mode: public

[ComfyUI-Manager] ComfyUI per-queue preview override detected (PR #11261). Manager's preview method feature is disabled. Use ComfyUI's --preview-method CLI option or 'Settings > Execution > Live preview method'.

### ComfyUI Revision: 1 [f59f71cf] *DETACHED | Released on '2025-12-30'

[ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/extension-node-map.json

[ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json

[ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/model-list.json

[ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/alter-list.json

[ComfyUI-Manager] default cache updated: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/github-stats.json

Error loading module AILab_QwenVL_GGUF_PromptEnhancer: No module named 'llama_cpp'



Import times for custom nodes:

   0.0 seconds: /opt/ComfyUI/custom_nodes/websocket_image_save.py

   0.1 seconds: /opt/ComfyUI/custom_nodes/ComfyUI-Manager

   0.5 seconds: /opt/ComfyUI/custom_nodes/ComfyUI-QwenVL



Context impl SQLiteImpl.

Will assume non-transactional DDL.

No target revision found.

Starting server



To see the GUI go to: http://*:8188

{"level":30,"time":1769011009412,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","msg":"Comfy UI started"}

{"level":30,"time":1769011009412,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","msg":"ComfyUI 0.7.0 started."}

{"level":30,"time":1769011009477,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","msg":"Registered workflow /workflow/img2img"}

{"level":30,"time":1769011009477,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","msg":"Registered workflow /workflow/qwen3-vl-experiment"}

{"level":30,"time":1769011009477,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","msg":"Registered workflow /workflow/txt2img"}

{"level":30,"time":1769011009561,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","msg":"ComfyUI fully ready in 10.19s"}

FETCH ComfyRegistry Data: 5/120

{"level":30,"time":1769011010791,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-1","req":{"method":"GET","url":"/health","host":"10.244.1.233:3000","remoteAddress":"::ffff:10.224.0.5","remotePort":35108},"msg":"incoming request"}

{"level":30,"time":1769011010793,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-1","res":{"statusCode":200},"responseTime":1.678925999905914,"msg":"request completed"}

FETCH ComfyRegistry Data: 10/120

FETCH ComfyRegistry Data: 15/120

FETCH ComfyRegistry Data: 20/120

{"level":30,"time":1769011020790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-2","req":{"method":"GET","url":"/health","host":"10.244.1.233:3000","remoteAddress":"::ffff:10.224.0.5","remotePort":45030},"msg":"incoming request"}

{"level":30,"time":1769011020791,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-2","res":{"statusCode":200},"responseTime":0.429451999720186,"msg":"request completed"}

FETCH ComfyRegistry Data: 25/120

FETCH ComfyRegistry Data: 30/120

FETCH ComfyRegistry Data: 35/120

{"level":30,"time":1769011030790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-3","req":{"method":"GET","url":"/health","host":"10.244.1.233:3000","remoteAddress":"::ffff:10.224.0.5","remotePort":41768},"msg":"incoming request"}

{"level":30,"time":1769011030790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-3","res":{"statusCode":200},"responseTime":0.3221490001305938,"msg":"request completed"}

FETCH ComfyRegistry Data: 40/120

FETCH ComfyRegistry Data: 45/120

FETCH ComfyRegistry Data: 50/120

FETCH ComfyRegistry Data: 55/120

{"level":30,"time":1769011040789,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-4","req":{"method":"GET","url":"/health","host":"10.244.1.233:3000","remoteAddress":"::ffff:10.224.0.5","remotePort":54420},"msg":"incoming request"}

{"level":30,"time":1769011040790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-4","res":{"statusCode":200},"responseTime":0.3270169999450445,"msg":"request completed"}

FETCH ComfyRegistry Data: 60/120

FETCH ComfyRegistry Data: 65/120

FETCH ComfyRegistry Data: 70/120

{"level":30,"time":1769011050790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-5","req":{"method":"GET","url":"/health","host":"10.244.1.233:3000","remoteAddress":"::ffff:10.224.0.5","remotePort":48618},"msg":"incoming request"}

{"level":30,"time":1769011050790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-5","res":{"statusCode":200},"responseTime":0.33953100023791194,"msg":"request completed"}

FETCH ComfyRegistry Data: 75/120

FETCH ComfyRegistry Data: 80/120

FETCH ComfyRegistry Data: 85/120

{"level":30,"time":1769011059836,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-6","req":{"method":"GET","url":"/health","host":"comfyui.local","remoteAddress":"::ffff:10.244.0.245","remotePort":36602},"msg":"incoming request"}

{"level":30,"time":1769011059836,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-6","res":{"statusCode":200},"responseTime":0.3211170001886785,"msg":"request completed"}

{"level":30,"time":1769011060790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-7","req":{"method":"GET","url":"/health","host":"10.244.1.233:3000","remoteAddress":"::ffff:10.224.0.5","remotePort":59568},"msg":"incoming request"}

{"level":30,"time":1769011060790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-7","res":{"statusCode":200},"responseTime":0.35903899976983666,"msg":"request completed"}

FETCH ComfyRegistry Data: 90/120

FETCH ComfyRegistry Data: 95/120

FETCH ComfyRegistry Data: 100/120

FETCH ComfyRegistry Data: 105/120

{"level":30,"time":1769011070790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-8","req":{"method":"GET","url":"/health","host":"10.244.1.233:3000","remoteAddress":"::ffff:10.224.0.5","remotePort":56480},"msg":"incoming request"}

{"level":30,"time":1769011070791,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-8","res":{"statusCode":200},"responseTime":0.5209230002947152,"msg":"request completed"}

FETCH ComfyRegistry Data: 110/120

FETCH ComfyRegistry Data: 115/120

FETCH ComfyRegistry Data: 120/120

FETCH ComfyRegistry Data [DONE]

[ComfyUI-Manager] default cache updated: https://api.comfy.org/nodes

FETCH DATA from: https://raw.githubusercontent.com/ltdrdata/ComfyUI-Manager/main/custom-node-list.json [DONE]

[ComfyUI-Manager] All startup tasks have been completed.

{"level":30,"time":1769011080790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-9","req":{"method":"GET","url":"/health","host":"10.244.1.233:3000","remoteAddress":"::ffff:10.224.0.5","remotePort":44198},"msg":"incoming request"}

{"level":30,"time":1769011080791,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-9","res":{"statusCode":200},"responseTime":0.5331540000624955,"msg":"request completed"}

{"level":30,"time":1769011090790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-a","req":{"method":"GET","url":"/health","host":"10.244.1.233:3000","remoteAddress":"::ffff:10.224.0.5","remotePort":37554},"msg":"incoming request"}

{"level":30,"time":1769011090790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-a","res":{"statusCode":200},"responseTime":0.4365039998665452,"msg":"request completed"}

{"level":30,"time":1769011100790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-b","req":{"method":"GET","url":"/health","host":"10.244.1.233:3000","remoteAddress":"::ffff:10.224.0.5","remotePort":43552},"msg":"incoming request"}

{"level":30,"time":1769011100790,"pid":1,"hostname":"comfyui-api-786bdbc-zz4nd","reqId":"req-b","res":{"statusCode":200},"responseTime":0.33777799969539046,"msg":"request completed"}

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Auto-Scaling ComfyUI-API and ComfyUI: Orchestrating GPU Workloads with Azure Kubernetes Service and KEDA

Introduction

Build the comfyui-api execution file

Dockerize the comfyui and comfyui-api app

Setup AKS cluster with GPU in-place

Run comfyui and comfyui-api with normal workload on AKS

Run autoscaling comfyui and comfyui-api on AKS and KEDA

Make it a test

Conclusion

Appendix - comfyui and comfyui-api logs

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Auto-Scaling ComfyUI-API and ComfyUI: Orchestrating GPU Workloads with Azure Kubernetes Service and KEDA

Introduction

Build the comfyui-api execution file

Dockerize the comfyui and comfyui-api app

Setup AKS cluster with GPU in-place

Run comfyui and comfyui-api with normal workload on AKS

Run autoscaling comfyui and comfyui-api on AKS and KEDA

Make it a test

Conclusion

Appendix - comfyui and comfyui-api logs

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