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A Deployment turns a cloud-saved ComfyUI workflow into a serverless API endpoint (identified by deployment_id). Use deployments when you want a stable endpoint with configurable hardware and autoscaling. RunComfy handles containerization, GPU allocation, and scaling—your application just calls the endpoint.
Note: If you don’t want to use the web UI, you can also create deployments via API. See Deployment Endpoints.

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Create a deployment (use web UI)

You can create a deployment from:
  • Deployments: Deployments → Deploy workflow as API
  • My Workflows: My Workflows → select a workflow → Deploy as API
  • Explore: Explore → select a workflow → Deploy as API

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1) Select a workflow

To deploy as an API, choose either:
  • a custom workflow from My Workflows (built/modified by you and Cloud Saved with dependencies), or
  • a community workflow from Explore (pre-saved and ready to deploy)
For guidance on building your own workflow, see Custom Workflows.

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2) Configure hardware

Choose GPU hardware based on your workflow’s VRAM requirements and performance needs. Test the workflow in a ComfyUI session first to estimate usage and avoid runtime errors. Typical VRAM tiers include:
  • 16GB: T4 or A4000
  • 24GB: A10G or A5000
  • 48GB: A6000
  • 48GB Plus: L40S or L40
  • 80GB: A100
  • 80GB Plus: H100
  • 141GB: H200
Exact GPU models and availability can vary. Refer to Pricing for the latest tiers and rates.

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3) Configure autoscaling

Autoscaling controls how many instances (running containerized copies of your workflow) can run in parallel and how quickly the deployment scales up/down. Common knobs:
  • Minimum instances (0–30)
    Baseline warm capacity. Setting this to 1 keeps one instance always warm (avoids cold starts but incurs ongoing cost). Use 0 to allow scale-to-zero (lowest idle cost), but the first request after idle may take a few minutes to start.
  • Maximum instances (1–60)
    Upper bound for parallel instances. Requests above capacity will queue. This caps cost and defines your concurrency ceiling. Need higher limits? Contact us.
  • Queue size (≥ 1)
    How many pending requests are allowed before the deployment tries to add capacity (up to max instances). Lower values prioritize latency; higher values buffer spikes.
  • Keep warm (seconds)
    How long an idle instance stays active after its last job before shutting down. Longer windows reduce cold-start frequency for bursty traffic, but increase idle cost.
Tip: A reasonable starting point for many apps is minimum 0, maximum 1, queue size 1, keep warm 60. Then tune based on real traffic.

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4) Deploy

Review your workflow, hardware, and scaling settings, then click Deploy. After creation, the deployment details page shows your deployment_id—use it in all inference calls. Next: Async Queue Endpoints.