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

# Custom Models

> Configure and use custom LLM models in LangWatch, including local inference servers and external endpoints like Databricks.

LangWatch supports connecting to any model that exposes an OpenAI-compatible API, including local inference servers (Ollama, vLLM, TGI), cloud deployments (Databricks, Azure ML), and custom APIs.

## Adding a Custom Model

1. Navigate to **Settings** in your project dashboard
2. Select **Model Provider** from the settings menu
3. Enable **Custom model**
4. Configure your model:

| Field          | Description                                               |
| -------------- | --------------------------------------------------------- |
| **Model Name** | A descriptive name for your model (e.g., `llama-3.1-70b`) |
| **Base URL**   | The endpoint URL for your model's API                     |
| **API Key**    | Authentication key (if required)                          |

<Tip>
  For local models that don't require authentication, enter any non-empty string as the API key.
</Tip>

### Example Configurations

**Ollama**

| Field    | Value                       |
| -------- | --------------------------- |
| Base URL | `http://localhost:11434/v1` |
| API Key  | `ollama`                    |

**vLLM**

| Field    | Value                      |
| -------- | -------------------------- |
| Base URL | `http://localhost:8000/v1` |
| API Key  | Your configured token      |

**Databricks**

| Field    | Value                                                        |
| -------- | ------------------------------------------------------------ |
| Base URL | `https://<workspace>.cloud.databricks.com/serving-endpoints` |
| API Key  | Your Databricks personal access token                        |

## Using Custom Models

Once configured, your custom models appear in the model selector throughout LangWatch, including the Prompt Playground and when configuring scenarios.

When referencing your custom model in code or API calls, use the format:

```
custom/<your-model-name>
```

For example, if you configured a model named `llama-3.1-70b`, reference it as `custom/llama-3.1-70b`.

## Related

* [LiteLLM Integration](/integration/python/integrations/lite-llm) - Unified interface for multiple providers
* [Tracking LLM Costs](/integration/python/tutorials/tracking-llm-costs) - Configure cost tracking
* [Prompt Playground](/prompt-management/prompt-playground) - Test prompts with custom models