> ## Documentation Index
> Fetch the complete documentation index at: https://docs.runcomfy.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Create a Deployment

Create a **Deployment** when you want to serve your trained LoRA through a **dedicated serverless endpoint** (stable `deployment_id`, GPU selection, autoscaling).

> If you don’t need a deployment (you just want on-demand inference and per-request billing), use the **Model API** instead.\
> See: **[Choose a LoRA inference API](/serverless-lora/api-types)**

***

## Prerequisites

You need a **LoRA Asset** in RunComfy Trainer:

* Train a LoRA in Trainer (it appears under **LoRA Assets**), or
* Import a LoRA (`.safetensors`). If available, also provide the training config so RunComfy can keep the base model + defaults consistent.

LoRA Assets live here: [Trainer > LoRA Assets](https://www.runcomfy.com/trainer/lora-assets)

***

## Create a Deployment

You can create a deployment from either place:

* From **LoRA Assets**, click **Deploy** / **Deploy Endpoint** (recommended — the LoRA is preselected)
* From **Deployments**, click **Create a deployment**

Both routes open the same configuration screen.

### 1) Name the deployment

Give it a human-readable name for dashboards and logs. API calls use the generated `deployment_id`.

### 2) Select a LoRA (base model is pinned)

Select the LoRA you want to serve. The deployment is pinned to the base model that LoRA was trained with.

### 3) Choose hardware

Pick a GPU tier based on your latency target and VRAM needs. Exact GPU models can vary by region/capacity, but the VRAM tier is what matters.

### 4) Configure autoscaling

Autoscaling controls how many instances can run in parallel and whether you keep warm capacity.

Common knobs:

* **Minimum instances (`0–30`)**: the baseline number of instances kept running. Set to `0` to minimize cost (idle deployments can stay at `$0/hr`), but the first request after idle may cold start. Set to `1+` to keep capacity warm and reduce cold starts, with ongoing runtime cost.
* **Maximum instances (`1–60`)**: the upper bound on instances that can run in parallel. This caps cost and effectively defines your concurrency ceiling; requests beyond capacity will wait in the queue until an instance is available. Need higher limits? Contact us.
* **Queue size (`≥ 1`)**: how many requests can remain pending while the deployment scales up (up to the maximum instances limit). Lower values prioritize latency (fail/pressure sooner), higher values prioritize cost (buffer bursts and scale gradually).
* **Keep warm duration (seconds)**: how long an instance stays up after finishing a request before scaling down. Shorter windows reduce idle cost; longer windows improve responsiveness for bursty traffic by avoiding frequent cold starts.

***

## Deploy

Click **Deploy**. When it finishes, copy the `deployment_id` from the deployment details page.

Next: **[Submit requests via the Async Queue Endpoints](/serverless-lora/async-queue-endpoints)**.