---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 6c3eeadb2d2f0866ad9c70480cc4a2827667ec0730fa5fdebf080b5973d0c3a5
  pageDirectory: concepts
  sources:
    - query-with-the-google-gemini-api-databricks-on-aws.md
  confidence: 0.9
  provenanceState: inferred
  freshnessStatus: unverified
  aliases:
    - external-model-routing-via-unified-ai-gateway
    - EMRVUAG
  citations:
    - file: query-with-the-google-gemini-api-databricks-on-aws.md
title: External Model Routing via Unified AI Gateway
description: The pattern of routing requests to external model providers (such as Google Gemini) through Databricks serving endpoints, enabling centralized auth, logging, and governance.
tags:
  - databricks
  - ai-gateway
  - model-serving
  - architecture
timestamp: "2026-06-19T20:05:34.567Z"
---

# External Model Routing via Unified AI Gateway

**External Model Routing via Unified AI Gateway** refers to the architectural pattern where a centralized gateway acts as a single entry point for routing requests to external AI models hosted outside the primary platform. This approach enables organizations to access third-party model providers—such as Google Gemini, OpenAI, Anthropic, or others—through a unified interface while maintaining consistent authentication, monitoring, and governance policies.

## Overview

The Unified AI Gateway provides a standardized endpoint that abstracts away the differences between various external model providers. Instead of each application or service directly connecting to individual model APIs, all requests flow through the gateway, which handles routing, authentication translation, and response forwarding. This pattern simplifies integration, improves security posture, and enables centralized observability. ^[query-with-the-google-gemini-api-databricks-on-aws.md]

## Architecture

The gateway sits between client applications and external model providers. Clients send requests to a single gateway endpoint, specifying the target model as a parameter. The gateway then:

1. Authenticates the client request using platform credentials
2. Translates the request into the format expected by the target external provider
3. Forwards the request to the appropriate external API
4. Returns the response back to the client

This architecture eliminates the need for each client to manage separate API keys, handle different authentication schemes, or track usage across multiple providers. ^[query-with-the-google-gemini-api-databricks-on-aws.md]

## Authentication and Credential Management

A key benefit of the Unified AI Gateway is centralized credential management. Clients authenticate once against the gateway using platform credentials (such as a Databricks token), and the gateway handles provider-specific authentication internally. This means:

- External provider API keys are stored and managed centrally, not distributed across clients
- Credential rotation for external providers happens in one place
- Access control policies can be enforced at the gateway level

For example, when routing to Google Gemini, the client authenticates with a Databricks token, and the gateway handles the Google-specific authentication. ^[query-with-the-google-gemini-api-databricks-on-aws.md]

## Client Integration

Clients interact with the Unified AI Gateway using the same SDKs and libraries they would use for direct provider access, but with modified configuration. The key changes are:

- **Base URL**: Pointed to the gateway endpoint instead of the provider's direct API
- **API Key**: Set to a platform-specific value (e.g., `"databricks"`) rather than the provider's actual key
- **Authentication Headers**: Platform credentials (e.g., Bearer token) are passed as custom headers

This approach allows existing codebases to switch to gateway-routed access with minimal changes. ^[query-with-the-google-gemini-api-databricks-on-aws.md]

## Example: Routing to Google Gemini

The following example demonstrates how a client routes requests to Google Gemini through a Unified AI Gateway. The client uses the Google GenAI SDK but configures it to point to the gateway endpoint:

```python
from google import genai
from google.genai import types
import os

DATABRICKS_TOKEN = os.environ.get('DATABRICKS_TOKEN')

client = genai.Client(
    api_key="databricks",
    http_options=types.HttpOptions(
        base_url="https://example.staging.cloud.databricks.com/serving-endpoints/gemini",
        headers={
            "Authorization": f"Bearer {DATABRICKS_TOKEN}",
        },
    ),
)

response = client.models.generate_content(
    model="databricks-gemini-2-5-pro",
    contents=[
        types.Content(
            role="user",
            parts=[types.Part(text="What is a mixture of experts model?")],
        ),
    ],
    config=types.GenerateContentConfig(
        max_output_tokens=256,
    ),
)

print(response.text)
```

Key points in this example:
- The `api_key` is set to `"databricks"` instead of a Google API key
- The `base_url` points to the gateway's serving endpoint for Gemini
- The `Authorization` header carries the Databricks token for platform authentication
- The `model` parameter specifies the gateway-routed model name (`databricks-gemini-2-5-pro`) ^[query-with-the-google-gemini-api-databricks-on-aws.md]

## Benefits

- **Unified access point**: Single endpoint for all external model providers
- **Centralized governance**: Consistent authentication, authorization, and auditing
- **Simplified credential management**: External API keys managed in one place
- **Provider abstraction**: Clients don't need to know provider-specific details
- **Monitoring and observability**: All external model usage visible through the gateway
- **Cost tracking**: Centralized point for tracking and allocating model usage costs

## Related Concepts

- API Gateway — The general architectural pattern for routing API requests
- [Model Serving Endpoints](/concepts/model-serving-endpoint.md) — Platform endpoints that expose models for inference
- External Model Integration — Patterns for connecting to third-party AI providers
- Authentication and Authorization — Security mechanisms for API access
- Service Mesh — Infrastructure layer for service-to-service communication

## Sources

- query-with-the-google-gemini-api-databricks-on-aws.md

# Citations

1. [query-with-the-google-gemini-api-databricks-on-aws.md](/references/query-with-the-google-gemini-api-databricks-on-aws-8dbd37cc.md)
