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

# Serve

> Expose your Weave ops and models as API endpoints for serving predictions and running inference in production.

This guide shows you how to expose a W\&B Weave Model as a FastAPI endpoint using `weave serve`, so you can query the model interactively and integrate it into production inference workflows.

To start a FastAPI server for any Weave Model, pass the Weave ref to `weave serve`. Replace `[REF]` with your Weave Model ref.

```bash theme={null}
weave serve [REF]
```

To query the model interactively, open the Swagger UI at `http://0.0.0.0:9996/docs`.

## Install FastAPI

`weave serve` uses FastAPI and Uvicorn to host the model, so you must install both packages before serving.

```bash theme={null}
pip install fastapi uvicorn
```

## Serve model

After installing the dependencies, start the server from a terminal. Replace `[YOUR-MODEL-REF]` with your Weave Model ref.

```bash theme={null}
weave serve [YOUR-MODEL-REF]
```

Get your model ref by navigating to the model and copying it from the UI. It should look like the following, where `[ENTITY]` is your W\&B entity, `[PROJECT-NAME]` is your project name, `[MODEL-NAME]` is the model name, and `[HASH]` is the model version hash:

```text theme={null}
weave://[ENTITY]/[PROJECT-NAME]/[MODEL-NAME]:[HASH]
```

To test the endpoint, open the Swagger UI, click the `predict` endpoint, then click **Try it out**. You now have a local FastAPI endpoint that serves predictions from your Weave Model.