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

# Call Your Deployment

> Connect to your deployed model using the OpenAI-compatible API.

Deployments expose an OpenAI-compatible chat completions endpoint. Point any OpenAI SDK client at the Inference base URL and set the `model` to your deployment's model path.

<CodeGroup>
  ```typescript TypeScript theme={"system"}
  import OpenAI from "openai";

  const client = new OpenAI({
    baseURL: "https://api.inference.net/v1",
    apiKey: process.env.INFERENCE_API_KEY,
  });

  const response = await client.chat.completions.create({
    model: "acme-corp/my-model",
    messages: [{ role: "user", content: "Hello, world!" }],
  });
  ```

  ```python Python theme={"system"}
  import os
  from openai import OpenAI

  client = OpenAI(
      base_url="https://api.inference.net/v1",
      api_key=os.environ["INFERENCE_API_KEY"],
  )

  response = client.chat.completions.create(
      model="acme-corp/my-model",
      messages=[{"role": "user", "content": "Hello, world!"}],
  )
  ```

  ```bash cURL theme={"system"}
  curl https://api.inference.net/v1/chat/completions \
    -H "Authorization: Bearer $INFERENCE_API_KEY" \
    -H "Content-Type: application/json" \
    -d '{
      "model": "acme-corp/my-model",
      "messages": [{"role": "user", "content": "Hello, world!"}]
    }'
  ```
</CodeGroup>

Structured outputs, function calling, and other chat completions features all work the same way. You can also tag requests with `x-inference-task-id` to group calls by objective — see [Tasks](/platform/gateway/tasks) for details.

## Where to find your model path

The model path is shown on your deployment's detail page in the dashboard. It's your team slug followed by the name you chose when creating the deployment (e.g. `acme-corp/my-model`).