> ## 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. # Choose a LoRA Inference API RunComfy Trainer lets you run inference with the **same LoRA + base model inference setup** in **two different ways**. * Both options keep **training and inference parity** (same base model, same setup, same defaults). * What changes is **how you run it** (on‑demand vs. your own dedicated endpoint) and **how you’re billed**.
On‑demand, no deployment, call a model\_id and pass your LoRA in the request body.
Billing: per request
Deploy your LoRA as a dedicated endpoint (a deployment) and call it with a deployment\_id.
Billing: GPU uptime, you can autoscale and scale down when idle *** ## How to decide (10 seconds) If your goal is simply: * “**I trained/imported a LoRA and I want to generate with it on top of the base model**” Start with the **Model API**. Only choose the **Serverless API (LoRA)** when you specifically need a **dedicated endpoint**, for example: * you want to **pick a GPU** tier for your workload * you need to control **how many runs can happen in parallel** (autoscaling / concurrency) * you want to **keep capacity warm** so the first request isn’t slow (reduce cold starts) * you need **more predictable response time** for production traffic > Tip: If you’re unsure, start with the Model API. You can always deploy a dedicated endpoint later without changing your prompts or workflow logic. *** ## Quick comparison | What you care about | Model API (on‑demand) | Serverless API (LoRA) (dedicated endpoint) | | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Do I need to deploy anything first? | **No** | **Yes**, create a Deployment from your LoRA Asset | | What ID do I call? | `model_id` | `deployment_id` | | Where do I find that ID? | In **Trainer > [Run LoRA](https://www.runcomfy.com/trainer/inference)**, select your LoRA’s **base model**, then open that base model page and copy its `model_id`. | In **Trainer > [Deployments](https://www.runcomfy.com/trainer/deployments)**, open your Deployment and copy the `deployment_id` from **Deployment details**. | | Where does the LoRA go? | You pass the LoRA in the **request body** (e.g. `lora.path`) | The LoRA is **already attached** to the Deployment (you don’t pass `lora.path`) | | Submit endpoint | `POST https://model-api.runcomfy.net/v1/models/{model_id}` | `POST https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/inference` | | Job flow | Async: submit > `request_id` > poll status/result | Async: submit > `request_id` > poll status/result | | Billing | **Per request** | **GPU uptime** (per‑second; can scale down to zero when idle) | *** ## Same async pattern, different IDs Both options are asynchronous. **Model API** ```text theme={null} POST https://model-api.runcomfy.net/v1/models/{model_id} -> returns request_id GET https://model-api.runcomfy.net/v1/requests/{request_id}/status GET https://model-api.runcomfy.net/v1/requests/{request_id}/result POST https://model-api.runcomfy.net/v1/requests/{request_id}/cancel ``` **Serverless API (LoRA) (Deployment)** ```text theme={null} POST https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/inference -> returns request_id GET https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/requests/{request_id}/status GET https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/requests/{request_id}/result POST https://api.runcomfy.net/prod/v2/deployments/{deployment_id}/requests/{request_id}/cancel ``` *** ## Option A — Model API (on‑demand, no deployment) Use this when you want the **fastest path** from LoRA to generation. ### What you do 1. In **Trainer > [Run LoRA](https://www.runcomfy.com/trainer/inference)**, select your LoRA’s **base model**, then open that base model page and copy its `model_id`. * This `model_id` represents the **inference setup** you’ll run (the model’s pipeline/workflow). 2. Call the **Model API** with that `model_id` and include your LoRA as an input parameter (for example `lora.path`). 3. Poll status and fetch results using the returned `request_id`. ### Key thing to remember The Model API will run **whatever inference setup** the `model_id` points to. For your trained LoRA inference: * `model_id` = the **base model’s** inference setup (from the model page) * LoRA = an **input** you provide on each request ### Where to go next * Start here: **[Model APIs Quickstart](/model-apis/quickstart)** * LoRA request fields + examples: **[LoRA Inputs (Trainer)](/model-apis/async-queue-endpoints#lora-inputs-trainer)** *** ## Option B — Serverless API (LoRA) (dedicated endpoint / Deployment) Use this when you need **more control over runtime behavior** (GPU choice, autoscaling, warm instances) and want to call your LoRA through a **stable dedicated endpoint**. ### What you do 1. In Trainer, turn your LoRA Asset into a **Deployment** (this creates the dedicated endpoint and pins the LoRA + base model + default settings). 2. Copy the `deployment_id` from the Deployment details page. 3. Submit inference to `POST /prod/v2/deployments/{deployment_id}/inference`. 4. Poll status and fetch results (same async pattern, but scoped under the deployment). ### Key thing to remember With a Deployment, the LoRA is already part of the endpoint. That means: * `deployment_id` selects the endpoint * Your request body only includes the **inputs defined by the deployment’s schema** (prompt, images, params, etc.) * You typically **do not** pass `lora.path` because the LoRA is already attached ### Where to go next * Overview: **[Serverless API (LoRA) Introduction](/serverless-lora/introduction)** * Full walkthrough: **[Serverless API (LoRA) Quickstart](/serverless-lora/quickstart)** * Request lifecycle details: **[Async Queue Endpoints](/serverless-lora/async-queue-endpoints)**