Google Vertex AI

Integrate Google Vertex AI models through the Adaline Proxy to automatically capture telemetry — requests, responses, token usage, latency, and costs — with minimal code changes. Vertex AI provides enterprise-grade access to Gemini models through your Google Cloud project.

Supported Models

Chat Models

Model	Description
`gemini-3.1-pro-preview`	Latest Gemini 3.1 Pro preview
`gemini-3-pro-preview`	Gemini 3 Pro preview
`gemini-3-flash-preview`	Gemini 3 Flash preview
`gemini-2.5-pro`	Gemini 2.5 Pro
`gemini-2.5-pro-preview-03-25`	Gemini 2.5 Pro March 2025 preview
`gemini-2.5-flash`	Gemini 2.5 Flash
`gemini-2.5-flash-preview-04-17`	Gemini 2.5 Flash April 2025 preview
`gemini-2.5-flash-lite`	Lightweight Gemini 2.5
`gemini-2.5-flash-lite-preview-09-2025`	Gemini 2.5 Flash Lite September 2025 preview
`gemini-2.0-flash`	Fast Gemini 2.0 model
`gemini-2.0-flash-exp`	Experimental Gemini 2.0 Flash
`gemini-2.0-flash-lite`	Lightweight Gemini 2.0
`gemini-1.5-pro`	Gemini 1.5 Pro, 1M token context
`gemini-1.5-pro-latest`	Gemini 1.5 Pro latest
`gemini-1.5-pro-002`	Gemini 1.5 Pro version 002
`gemini-1.5-pro-001`	Gemini 1.5 Pro version 001
`gemini-1.5-flash`	Gemini 1.5 Flash
`gemini-1.5-flash-latest`	Gemini 1.5 Flash latest
`gemini-1.5-flash-002`	Gemini 1.5 Flash version 002
`gemini-1.5-flash-001`	Gemini 1.5 Flash version 001

Embedding Models

Model	Description
`text-embedding-004`	Latest text embedding model
`text-multilingual-embedding-002`	Multilingual embedding model
`textembedding-gecko@003`	Gecko embedding model
`textembedding-gecko-multilingual@001`	Multilingual Gecko

Proxy Base URL

https://gateway.adaline.ai/v1/vertex

Prerequisites

A Google Cloud project with Vertex AI API enabled
GCP credentials configured (application default credentials or service account)
An Adaline API key, project ID, and prompt ID

Chat Completions

Complete Chat

from google import genai
from google.genai import types

client = genai.Client(
    http_options={
        "base_url": "https://gateway.adaline.ai/v1/vertex",
        "headers": {
            "adaline-api-key": "your-adaline-api-key",
            "adaline-project-id": "your-project-id",
            "adaline-prompt-id": "your-prompt-id",
        },
    },
    vertexai=True,
    project="your-gcp-project-id",
    location="us-central1",
)

response = client.models.generate_content(
    model="gemini-1.5-pro",
    contents="What are the advantages of using Google Cloud for AI workloads?",
    config=types.GenerateContentConfig(
        http_options=types.HttpOptions(
            headers={
                "adaline-trace-name": "vertex-chat-completion"  # Optional
            }
        )
    )
)

print(response.text)

Vertex AI uses vertexai=True and requires a GCP project and location, unlike Google AI Studio which only needs an API key. Headers are passed via http_options.

Stream Chat

from google import genai
from google.genai import types

client = genai.Client(
    http_options={
        "base_url": "https://gateway.adaline.ai/v1/vertex",
        "headers": {
            "adaline-api-key": "your-adaline-api-key",
            "adaline-project-id": "your-project-id",
            "adaline-prompt-id": "your-prompt-id",
        },
    },
    vertexai=True,
    project="your-gcp-project-id",
    location="us-central1",
)

stream = client.models.generate_content_stream(
    model="gemini-1.5-pro",
    contents="Explain the concept of serverless computing in detail.",
    config=types.GenerateContentConfig(
        http_options=types.HttpOptions(
            headers={
                "adaline-trace-name": "vertex-stream-chat"  # Optional
            }
        )
    )
)

for chunk in stream:
    if hasattr(chunk, 'text') and chunk.text:
        print(chunk.text, end="")

Embeddings

from google import genai
from google.genai import types

client = genai.Client(
    http_options={
        "base_url": "https://gateway.adaline.ai/v1/vertex",
        "headers": {
            "adaline-api-key": "your-adaline-api-key",
            "adaline-project-id": "your-project-id",
            "adaline-prompt-id": "your-prompt-id",
        },
    },
    vertexai=True,
    project="your-gcp-project-id",
    location="us-central1",
)

response = client.models.embed_content(
    model="text-embedding-004",
    contents="The quick brown fox jumps over the lazy dog",
    config=types.EmbedContentConfig(
        http_options=types.HttpOptions(
            headers={
                "adaline-trace-name": "vertex-embedding"  # Optional
            }
        )
    )
)

Google AI Studio vs Vertex AI

Feature	Google AI Studio	Vertex AI
Authentication	API key	GCP credentials (IAM)
Proxy base URL	`gateway.adaline.ai/v1/google`	`gateway.adaline.ai/v1/vertex`
SDK parameter	`api_key="..."`	`vertexai=True, project="...", location="..."`
Best for	Prototyping, personal projects	Enterprise, production workloads
Data residency	No control	Region-specific

Next Steps

Multi-Step Workflows — RAG pipelines, multi-step generation, and conversational agents
Headers Reference — Complete header documentation

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Frameworks

Google Vertex AI

Google Vertex AI

Supported Models

Proxy Base URL

Prerequisites

Chat Completions

Complete Chat

Stream Chat

Embeddings

Google AI Studio vs Vertex AI

Next Steps

Back to Integrations

​Google Vertex AI

​Supported Models

​Proxy Base URL

​Prerequisites

​Chat Completions

​Complete Chat

​Stream Chat

​Embeddings

​Google AI Studio vs Vertex AI

​Next Steps

Back to Integrations

Google Vertex AI

Supported Models

Proxy Base URL

Prerequisites

Chat Completions

Complete Chat

Stream Chat

Embeddings

Google AI Studio vs Vertex AI

Next Steps