# Custom LLMs
Custom LLMs lets you **connect** your own language model providers to InteractiveAI. Once connected, these models become available in the **Playground** for prompt testing and in **Evaluators** for automated quality assessment. Your provider charges you directly based on usage, while InteractiveAI simply routes requests through your credentials.
This section serves two purposes. **Connections** store your API keys for providers like OpenAI, Anthropic, and Google AI Studio. **Configurations** define pricing and tokenization settings so InteractiveAI can accurately calculate costs when you use models through direct API integrations rather than the InteractiveAI Router.
***
### Connections
Connections store the **credentials** InteractiveAI uses to communicate with external LLM providers. Each connection links a provider name to an API key and endpoint configuration.
#### Viewing Connections
The Connections tab displays all configured providers in a table showing:
| Column | Description |
| ------------ | -------------------------------------------------------------- |
| **Provider** | Display name you assigned to this connection |
| **Adapter** | The provider type (e.g., `openai`, `google-ai-studio`) |
| **Base URL** | API endpoint (`default` uses the provider's standard endpoint) |
| **API Key** | Masked key showing the last few characters |
#### Adding a Connection
Click the **+** button in the top-right corner to open the Add LLM Connection modal.
| Field | Description |
| ----------------- | ------------------------------------------------------------------------------------------------------------------------- |
| **Provider name** | A display name to identify this connection within InteractiveAI (e.g., "OpenAI Production", "Gemini") |
| **LLM adapter** | The provider type that determines the API schema. Options include `openai`, `google-ai-studio`, `anthropic`, and others |
| **API Base URL** | Leave as `default` to use the provider's standard endpoint, or enter a custom URL for self-hosted or proxy configurations |
| **API Key** | Your provider's API key. Stored encrypted in the database |
| **Extra Headers** | Optional HTTP headers to include with requests (also stored encrypted) |
{% hint style="info" %}
You can create multiple connections for the same provider. This is useful for separating production and development keys, or for connecting to different accounts.
{% endhint %}
***
### Configurations
Configurations define model metadata for **cost tracking** and **tokenization**. When you use models through direct API integrations (not through the InteractiveAI Router), these definitions tell the platform how to calculate costs based on token usage.
#### Viewing Configurations
The Configurations tab displays all model definitions in a table showing:
| Column | Description |
| ------------------- | ---------------------------------------------------------- |
| **Model Name** | Identifier for the model (e.g., `gemini-2.5-pro`, `gpt-5`) |
| **Prices per unit** | Number of pricing rules configured |
| **Provider** | Who maintains this definition (User or System) |
| **Match Pattern** | Regex pattern used to identify this model in traces |
| **Tokenizer** | Tokenization method for counting tokens |
| **Created** | When the definition was added |
| **Last Used** | Most recent usage of this model |
#### Adding a Model Definition
Click **Add Model Definition** to open the configuration modal.
**Model Details**
| Field | Description |
| ----------------- | ------------------------------------------------------------------------------------------------------------------- |
| **Model Name** | The model identifier as it appears in API calls (e.g., `gpt-4-turbo`, `claude-3-opus`) |
| **Match Pattern** | A regex pattern to match this model in your traces. For example, `(?i)^(gpt-5)$` matches "gpt-5" case-insensitively |
| **Tokenizer** | The tokenization method used to count tokens. Select the appropriate tokenizer for accurate cost calculation |
**Prices**
Set prices per usage type. Usage types must exactly match the keys in your ingested usage details.
* For OpenAI and compatible providers, typical usage types are:
* `input` — Price per input token
* `output` — Price per output token
* For Anthropic models, you may also configure:
* `input` — Price per input token
* `output` — Price per output token
* `cache_read` — Price per cached input token
Click **+ Add Price** to add additional usage types as needed.
**Price Preview**
The modal displays a live preview showing your configured prices at different scales:
| Usage Type | Per Unit | Per 1K | Per 1M |
| ---------- | --------- | ------ | ------ |
| input | $0.000001 | $0.001 | $1 |
| output | $0.000002 | $0.002 | $2 |
This helps you verify that pricing is configured correctly before saving.
***
### When to Use Custom LLMs
Custom LLMs are essential when you want to:
* **Use the Playground:** Test prompts interactively against your preferred models
* **Run Evaluators:** Power LLM-as-a-Judge evaluations with your own model credentials
* **Track costs accurately:** Define pricing for models used through direct integrations so dashboards reflect actual spending
{% hint style="warning" %}
If you're using the **InteractiveAI Router** for model access, you don't need to configure Custom LLMs, the Router handles provider connections and cost tracking automatically.
{% endhint %}
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