> ## 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.

# Quickstart

This guide shows how to **deploy your trained LoRA (trained with its base model) as a serverless endpoint** and run your first inference request.

> Want to run LoRA inference **without deploying**? Use the **Model API** instead.\
> Start here: **[Choose a LoRA inference API](/serverless-lora/api-types)**

***

## Step 1: Make sure you have a LoRA Asset

A **LoRA Asset** comes from either:

* training in RunComfy Trainer, or
* importing a `.safetensors` LoRA (plus its training config)

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

<img src="https://mintcdn.com/inceptionsaiinc/g_bHNv4-HIjrI1yw/docs-image/lora.webp?fit=max&auto=format&n=g_bHNv4-HIjrI1yw&q=85&s=5c0e21ad4ddee58760bc7c310a1093f3" alt="Alt LoRA Asset" width="2865" height="804" data-path="docs-image/lora.webp" />

***

## Step 2: Create a Deployment

From the LoRA Asset page, click **Deploy** (or **Deploy Endpoint**) to create a Deployment.

<img src="https://mintcdn.com/inceptionsaiinc/g_bHNv4-HIjrI1yw/docs-image/lora-deploy.webp?fit=max&auto=format&n=g_bHNv4-HIjrI1yw&q=85&s=7dd2f52d4cfcac446c4ddf0cdc04442b" alt="Alt LoRA Asset" width="2865" height="804" data-path="docs-image/lora-deploy.webp" />

During deployment you can choose:

* **hardware** (GPU tier)
* **autoscaling** (scale to zero, max instances, keep-warm)

When the deployment is ready, copy the `deployment_id` from the Deployment details page.

***

## Step 3: Authenticate

All API calls require a Bearer token:

`Authorization: Bearer <token>`

Get your API token from your [Profile](https://www.runcomfy.com/profile) page.

***

## Step 4: Submit an inference request

The exact request body depends on the pipeline attached to your Deployment.\
Use the Deployment **API** tab (or its request schema in the dashboard) as the source of truth.

```bash theme={null}
curl --request POST \
  --url "https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/inference" \
  --header "Content-Type: application/json" \
  --header "Authorization: Bearer <token>" \
  --data '{
    "prompt": "A cinematic portrait photo",
    "...": "other fields depend on your deployment"
  }'
```

**Response example:**

```json theme={null}
{
  "request_id": "{request_id}",
  "status_url": "https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/requests/{request_id}/status",
  "result_url": "https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/requests/{request_id}/result",
  "cancel_url": "https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/requests/{request_id}/cancel"
}
```

***

## Step 5: Poll status, then fetch results

Check status:

```bash theme={null}
curl --request GET \
  --url "https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/requests/{request_id}/status" \
  --header "Authorization: Bearer <token>"
```

Fetch results:

```bash theme={null}
curl --request GET \
  --url "https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/requests/{request_id}/result" \
  --header "Authorization: Bearer <token>"
```

***

## Next steps

* Request lifecycle + file uploads: **[Async Queue Endpoints](/serverless-lora/async-queue-endpoints)**
* Deployment settings: **[Create a Deployment](/serverless-lora/create-a-deployment)**
* Troubleshooting: **[Error Codes](/serverless-lora/error-codes)**