> ## Documentation Index > Fetch the complete documentation index at: https://docs.getbifrost.ai/llms.txt > Use this file to discover all available pages before exploring further. # Databricks AI Gateway > Route requests through Databricks AI Gateway using Unified (MLflow) or Native (Anthropic Messages) APIs as custom providers in Bifrost ## Overview [Databricks AI Gateway](https://docs.databricks.com/en/ai-gateway/index.html) (Beta) is a governance layer on top of Databricks Model Serving that adds rate limiting, usage tracking, and inference logging to your LLM endpoints. Bifrost connects to AI Gateway endpoints as custom providers. ### Unified vs Native APIs AI Gateway exposes two categories of APIs on every endpoint: * **Unified APIs** - Provider-agnostic, OpenAI-compatible interfaces powered by MLflow. You can swap the underlying model without changing client code. Path: `/mlflow/v1/chat/completions`. * **Native APIs** - Provider-specific interfaces that give full access to a provider's latest features. For Anthropic, the path is `/anthropic/v1/messages`. In Bifrost, each API category maps to a different custom provider base format: | API Category | Bifrost Base Format | Chat | Chat (stream) | Responses | Responses (stream) | Text | Coding Agents | List Models | | ------------------------------- | ------------------- | ---- | ------------- | --------- | ------------------ | ---- | ------------- | --------------------- | | **Unified** (MLflow) | `openai` | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ (via Unity Catalog) | | **Native** (Anthropic Messages) | `anthropic` | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | The **Unified (MLflow) API** is a pure chat completions interface - it does **not** support the Responses API. Coding agents like Claude Code, Cursor, or Codex CLI that depend on the Responses API will **not** work through the Unified API. Use the **Native Anthropic Messages API** if you need Responses API support, coding agent compatibility, or text completions. ### Prerequisites Before configuring Bifrost, you need: 1. A Databricks workspace with **Unity Catalog enabled** and AI Gateway access turned on by an account admin via **Account Console > Previews** 2. An AI Gateway endpoint with at least one model destination - create one from the **AI Gateway** page in the Databricks sidebar 3. The endpoint's AI Gateway URL - visible at the top of the endpoint overview page, in the format: ``` https://.ai-gateway.cloud.databricks.com ``` 4. A Databricks Personal Access Token (PAT) - generate one from **Settings > Developer > Access tokens** in your Databricks workspace, or click **Generate Access Token** at the bottom of the endpoint page Databricks AI Gateway endpoint overview page showing the API format dropdown and Generate Access Token button *** # 1. Unified API (MLflow Chat Completions) The Unified API exposes an OpenAI-compatible chat completions interface through AI Gateway's MLflow layer. Use this when you only need chat completions. ### How it works AI Gateway exposes every endpoint at a `/mlflow/v1/chat/completions` path. Because this follows the OpenAI spec, Bifrost treats it as an OpenAI-compatible custom provider. The full endpoint URL looks like: ``` https://.ai-gateway.cloud.databricks.com/mlflow/v1/chat/completions ``` You register only the base portion (`/mlflow`) as the custom provider's Base URL - Bifrost appends the standard `/v1/chat/completions` path automatically. ## Step 1: Create the Custom Provider In Bifrost, go to **Models > Model Providers** in the sidebar. Click **Add New Provider** and select **Custom provider...** at the bottom of the dropdown. In the **Add Custom Provider** dialog, fill in: | Field | Value | | --------------- | --------------------------------------------------------------- | | **Name** | Your choice (e.g., `databricks-mlflow`) | | **Base Format** | Select `OpenAI` from the dropdown | | **Base URL** | `https://.ai-gateway.cloud.databricks.com/mlflow` | | **Is Keyless?** | Toggle on | ## Step 2: Configure List Models (Optional) The default `/v1/models` path does not work against the AI Gateway URL. To enable model listing, point it at the **Unity Catalog** API on your Databricks workspace instead. In the **Allowed Request Types** section of the dialog: 1. Find the **List Models** toggle (make sure it's enabled) 2. Click the **settings icon** (gear) next to List Models - this opens the **Custom Path or URL** popover 3. Enter your workspace's Unity Catalog models endpoint: ``` https:///api/2.1/unity-catalog/models ``` The Unity Catalog URL uses your **Databricks workspace URL** (e.g., `https://adb-1234567890.azuredatabricks.net`), which is a different host from the AI Gateway URL (`*.ai-gateway.cloud.databricks.com`). Add Custom Provider dialog configured for MLflow with the List Models custom path popover showing the Unity Catalog URL Click **Add** to save the custom provider. ## Step 3: Add the Authorization Header After saving, your new provider appears in the **Configured Providers** list on the left. Select it, then click **Edit Provider Config** (the settings icon in the top-right corner) to open the provider configuration panel. 1. Switch to the **Network** tab 2. Scroll down to the **Extra Headers** table 3. Add a new row: * **Name** column: `Authorization` * **Value** column: `Bearer ` 4. Click **Save Network Configuration** Provider configuration Network tab showing the Authorization header in Extra Headers for the MLflow provider ## Step 4: Send Requests Use your custom provider prefix with any model name registered as a destination on your AI Gateway endpoint: ```bash theme={null} curl -X POST http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "databricks-mlflow/", "messages": [{"role": "user", "content": "Hello!"}] }' ``` ```go theme={null} response, err := client.ChatCompletionRequest( schemas.NewBifrostContext(ctx, schemas.NoDeadline), &schemas.BifrostChatRequest{ Provider: "databricks-mlflow", Model: "", Input: messages, }, ) ``` *** # 2. Native API (Anthropic Messages) The Native API exposes an Anthropic-compatible messages interface through AI Gateway. Use this when you need the Responses API, text completions, or coding agent support (Claude Code, Cursor, Codex CLI). ### How it works AI Gateway exposes every endpoint at an `/anthropic/v1/messages` path that follows the Anthropic API spec. Bifrost treats this as an Anthropic-compatible custom provider. The full endpoint URL looks like: ``` https://.ai-gateway.cloud.databricks.com/anthropic/v1/messages ``` You register only the base portion (`/anthropic`) as the custom provider's Base URL - Bifrost appends the standard Anthropic paths automatically. ## Step 1: Create the Custom Provider In Bifrost, go to **Models > Model Providers** in the sidebar. Click **Add New Provider** and select **Custom provider...** at the bottom of the dropdown. In the **Add Custom Provider** dialog, fill in: | Field | Value | | --------------- | ------------------------------------------------------------------ | | **Name** | Your choice (e.g., `databricks-anthropic`) | | **Base Format** | Select `Anthropic` from the dropdown | | **Base URL** | `https://.ai-gateway.cloud.databricks.com/anthropic` | | **Is Keyless?** | Toggle on | ## Step 2: Disable List Models AI Gateway's model listing endpoint uses an OpenAI-compatible format, which is incompatible with the Anthropic base format. You must disable it. In the **Allowed Request Types** section of the dialog, find the **List Models** toggle and turn it **off**. Add Custom Provider dialog configured for Anthropic Messages with List Models toggled off Click **Add** to save the custom provider. ## Step 3: Add the Authorization Header After saving, select your new provider from the **Configured Providers** list and click **Edit Provider Config** to open the configuration panel. 1. Switch to the **Network** tab 2. Scroll down to the **Extra Headers** table 3. Add a new row: * **Name** column: `Authorization` * **Value** column: `Bearer ` 4. Click **Save Network Configuration** Provider configuration Network tab showing the Authorization header in Extra Headers for the Anthropic provider ## Step 4: Send Requests Use your custom provider prefix with any model name registered as a destination on your AI Gateway endpoint: ```bash theme={null} curl -X POST http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "databricks-anthropic/", "messages": [{"role": "user", "content": "Hello!"}] }' ``` ```go theme={null} response, err := client.ChatCompletionRequest( schemas.NewBifrostContext(ctx, schemas.NoDeadline), &schemas.BifrostChatRequest{ Provider: "databricks-anthropic", Model: "", Input: messages, }, ) ``` ### Coding Agent Compatibility The Native Anthropic Messages API works with Claude Code and other coding agents that depend on the Responses API. Point your coding agent at your Bifrost instance and use the `databricks-anthropic/` prefix to route through your AI Gateway endpoint. *** ## Choosing the Right API | Consideration | Unified (MLflow) | Native (Anthropic Messages) | | -------------------------------------------------------- | --------------------- | --------------------------- | | **Chat Completions** | ✅ | ✅ | | **Streaming** | ✅ | ✅ | | **Responses API** | ❌ | ✅ | | **Text Completions** | ❌ | ✅ | | **Coding Agents** (Claude Code, Cursor, Codex) | ❌ | ✅ | | **List Models** | ✅ (via Unity Catalog) | ❌ | | **Provider-agnostic** (swap models without code changes) | ✅ | ❌ | | **Bifrost Base Format** | `openai` | `anthropic` | You can create **two separate custom providers** - one per API category - pointing to the same AI Gateway endpoint. Use the Unified provider for chat completions with model listing, and the Native Anthropic provider for Responses API or coding agents. *** ## Reference Links * [Databricks AI Gateway Documentation](https://docs.databricks.com/en/ai-gateway/index.html) * [Create an AI Gateway Endpoint](https://docs.databricks.com/en/ai-gateway/create-endpoint.html) * [Databricks Personal Access Tokens](https://docs.databricks.com/en/dev-tools/auth/pat.html) * [Custom Providers in Bifrost](/providers/custom-providers)