> ## 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.

# Introduction

<Warning>
  **API v1 is being deprecated — please migrate to v2.** All new integrations should use the `/prod/v2/...` endpoints. v1 remains available for now but will not receive new features and will be retired in a future release.
</Warning>

**Serverless API (LoRA)** lets you deploy a **LoRA** as a **dedicated, scalable endpoint** (a *Deployment*) and run inference through a standard async queue API.

It is built on the same serverless system as **Serverless API (ComfyUI)** — the difference is simply what you deploy:

* **Serverless API (ComfyUI):** you deploy a *ComfyUI workflow*
* **Serverless API (LoRA):** you deploy *your trained LoRA* (pinned to its base model + default inference config)

> If you only want to run LoRA inference **without creating a deployment**, use the **Model API** instead.\
> Start here: **[Choose a LoRA inference API](/serverless-lora/api-types)**

***

## Key concepts

Serverless API (LoRA) revolves around three objects:

### LoRA Asset

A **LoRA Asset** is the output of training or importing a LoRA in RunComfy Trainer. It includes:

* LoRA adapter weights (`.safetensors`)
* training metadata (for example the base model reference)
* the defaults Trainer uses for inference

### Deployment

A **Deployment** is the serverless endpoint you call from your app.

When you create a Deployment from a LoRA Asset, RunComfy:

* **pins the base model checkpoint** the LoRA was trained on
* attaches the LoRA weights
* loads the same default inference setup you used in Trainer

This is what gives you “training and inference parity”: the deployed endpoint starts from the same setup that produced your training samples.

### Request

A **request** is a single inference job against a Deployment.

You submit a request, get back a `request_id`, then poll status/results (or use webhooks).

***

## Where to find things in the UI

* [LoRA Assets](https://www.runcomfy.com/trainer/lora-assets)
* [Deployments](https://www.runcomfy.com/trainer/deployments)
* [Requests](https://www.runcomfy.com/trainer/requests)

***

## Typical workflow

1. **Train or import a LoRA** in Trainer > you get a LoRA Asset
2. **Create a Deployment** (choose hardware + autoscaling)
3. **Submit inference** to the Deployment endpoint (`POST …/inference`)
4. **Poll status** (`GET …/status`) and **fetch outputs** (`GET …/result`)

Next step: **[Quickstart](/serverless-lora/quickstart)**

> Want to train a LoRA model yourself? Start with **[Trainer APIs Quickstart](/trainer-apis/quickstart)**.