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ComfyUI can run node-based generative-AI workflows locally or on remote GPUs and accept workflows programmatically through its server API. In production, queue behavior, model files, custom-node security, output storage and user isolation matter alongside VRAM.
Yes. Current ComfyUI server documentation shows workflows can be submitted over HTTP, outputs retrieved and real-time execution progress followed over WebSocket. Workflows should be exported in API format for programmatic use.
The client submits workflow JSON to the queue, ComfyUI executes the graph on GPU and returns progress/output over HTTP/WebSocket.
A workflow may load text encoders, VAE, ControlNet, LoRA, video models or multiple checkpoints. Resolution and batch size also change peak VRAM.
Custom nodes execute Python code, so installing untrusted packages on a production host is risky. Use reverse proxy authentication, upload/output limits and a dedicated service user.
Verify endpoint paths against the current ComfyUI server routes.
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nvidia-smicurl -s http://127.0.0.1:8188/system_stats | headcurl -s http://127.0.0.1:8188/queue | headss -lntp | grep ':8188'ps aux | grep '[m]ain.py'Checkpoints, VAEs, LoRAs, ControlNets and outputs can consume hundreds of GB. Without cache/output lifecycle policies, NVMe can fill quickly.
Yes. Official server docs cover prompt submission, queue/history and WebSocket progress.
It depends on model, resolution, batch, ControlNet/LoRA and video nodes.
It depends on source. They execute Python code, so production should allow only trusted, pinned nodes.
Share workflow JSON, models, resolution, batch and daily generations; we can size GPU/VRAM/NVMe.