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Google Vertex AI provides enterprise-grade access to Gemini models with enhanced security, compliance, and control. Connecting Vertex AI to Orq.ai provides enterprise Gemini capabilities with service account authentication, project-level billing, and data residency controls.

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Set Up an API Key

To use Vertex AI with Orq.ai, create a service account with appropriate permissions:
1

Create Service Account

  1. Go to Google Cloud Console
  2. Navigate to IAM & Admin > Service Accounts
  3. Click Create Service Account
  4. Enter a name (e.g., “orq-vertex-ai”)
  5. Grant the following roles:
    • Service Account Token Creator
    • Vertex AI User
  6. Click Create and Continue
  7. Click Done
2

Create Service Account Key

  1. Find the service account in the list
  2. Click the Actions menu (three dots)
  3. Select Manage Keys
  4. Click Add Key > Create New Key
  5. Select JSON format
  6. Click Create to download the key file
3

Configure in Orq.ai

  1. Navigate to AI Gateway > BYOK
  2. Find Google Vertex AI in the list
  3. Click the Configure button
  4. Select Setup your own API Key
  5. Enter configuration name (e.g., “Vertex AI Production”)
  6. Paste the service account JSON in the Deployment JSON field (see format below)
  7. Click Save to complete the setup
Vertex AI configuration modal

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Deployment JSON Format

The deployment JSON must include the service account credentials, project ID, and region: 
{
  "projectId": "my-project-123456",
  "location": "us-central1",
  "serviceAccount": {
    "type": "service_account",
    "project_id": "my-project-123456",
    "private_key_id": "afd17083ecd5184b5ca880e70eb84c2e4c382f14",
    "private_key": "-----BEGIN PRIVATE KEY-----\n...=\n-----END PRIVATE KEY-----\n",
    "client_email": "vertex-ai@my-project-123456.iam.gserviceaccount.com",
    "client_id": "000000000000000000000",
    "auth_uri": "https://accounts.google.com/o/oauth2/auth",
    "token_uri": "https://oauth2.googleapis.com/token",
    "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
    "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/vertex-ai%40my-project-123456.iam.gserviceaccount.com",
    "universe_domain": "googleapis.com"
  }
}
Project ID: Find the Google Cloud Project ID at the top of the Google Cloud Console.Location: Common regions include us-central1, europe-west1, asia-northeast1. Choose based on data residency requirements.

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Available Models

The AI Gateway supports all current Vertex AI Gemini models. Here are the most commonly used:
ModelContextBest For
google/gemini-2.5-pro-preview1MLatest preview, most advanced
google/gemini-2.5-pro1MLatest stable, most capable
google/gemini-2.5-flash1MFast, balanced performance
google/gemini-2.0-flash-0011MStable, reliable
For a complete and up-to-date list of all available Vertex AI models, see Supported Models.
Use google/gemini-2.5-pro for the latest stable model, or google/gemini-2.5-flash for the best balance of performance and cost.

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Quick Start

Access Vertex AI Gemini models through the AI Gateway. 
curl -X POST https://api.orq.ai/v3/router/responses \
  -H "Authorization: Bearer $ORQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-2.5-pro",
    "input": "Explain quantum computing in simple terms"
  }'

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Using the AI Gateway

Access Vertex AI Gemini models through the AI Gateway with enterprise-grade security, advanced chat completions, streaming, and intelligent model routing. All Vertex AI models are available with consistent formatting and automatic request logging.
Vertex AI models use the provider slug format: google/model-name. For example: google/gemini-2.5-pro

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Prerequisites

Before making requests to the AI Gateway, configure the environment and install the required SDKs. Endpoint 
POST https://api.orq.ai/v3/router/responses
Required Headers Include the following headers in all requests: 
Authorization: Bearer $ORQ_API_KEY
Content-Type: application/json
Getting an API Key:
  1. Go to API Keys
  2. Click Create API Key and copy it
  3. Store it in your environment as ORQ_API_KEY
SDK Installation Install the OpenAI SDK for your language (compatible with Vertex AI models): 
npm install openai
# or
yarn add openai
If existing OpenAI code is already functioning, change only the base_url and api_key to the AI Gateway endpoint and ORQ_API_KEY.

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Basic Usage

Send messages to Vertex AI Gemini models and get intelligent responses: 
curl -X POST https://api.orq.ai/v3/router/responses \
  -H "Authorization: Bearer $ORQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-2.5-pro",
    "instructions": "You are a helpful assistant that explains complex concepts simply.",
    "input": "Explain machine learning"
  }'

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Streaming

Stream responses for real-time output and improved user experience: 
curl -X POST https://api.orq.ai/v3/router/responses \
  -H "Authorization: Bearer $ORQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-2.5-pro",
    "input": "Write a short poem about the ocean",
    "stream": true
  }'

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Function Calling

Vertex AI Gemini models support function calling for structured interactions: 
curl -X POST https://api.orq.ai/v3/router/responses \
  -H "Authorization: Bearer $ORQ_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "google/gemini-2.5-pro",
    "input": "What is the weather in San Francisco?",
    "tools": [{
      "type": "function",
      "name": "get_weather",
      "description": "Get the current weather in a location",
      "parameters": {
        "type": "object",
        "properties": {
          "location": { "type": "string", "description": "The city and state, e.g. San Francisco, CA" },
          "unit": { "type": "string", "enum": ["celsius", "fahrenheit"] }
        },
        "required": ["location"]
      }
    }]
  }'

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Automatic Request Logging

All requests made through the AI Gateway are automatically logged to the dashboard. The dashboard shows:
  • Request details: Model used, tokens, latency
  • Cost tracking: Per-request and aggregate costs
  • Error monitoring: Failed requests with error messages
  • Performance metrics: Response times and throughput
No additional configuration is needed. Logging happens automatically.

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Reference