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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.

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Supported Models

Chat Models
ModelDescription
gemini-3.1-pro-previewLatest Gemini 3.1 Pro preview
gemini-3-pro-previewGemini 3 Pro preview
gemini-3-flash-previewGemini 3 Flash preview
gemini-2.5-proGemini 2.5 Pro
gemini-2.5-pro-preview-03-25Gemini 2.5 Pro March 2025 preview
gemini-2.5-flashGemini 2.5 Flash
gemini-2.5-flash-preview-04-17Gemini 2.5 Flash April 2025 preview
gemini-2.5-flash-liteLightweight Gemini 2.5
gemini-2.5-flash-lite-preview-09-2025Gemini 2.5 Flash Lite September 2025 preview
gemini-2.0-flashFast Gemini 2.0 model
gemini-2.0-flash-expExperimental Gemini 2.0 Flash
gemini-2.0-flash-liteLightweight Gemini 2.0
gemini-1.5-proGemini 1.5 Pro, 1M token context
gemini-1.5-pro-latestGemini 1.5 Pro latest
gemini-1.5-pro-002Gemini 1.5 Pro version 002
gemini-1.5-pro-001Gemini 1.5 Pro version 001
gemini-1.5-flashGemini 1.5 Flash
gemini-1.5-flash-latestGemini 1.5 Flash latest
gemini-1.5-flash-002Gemini 1.5 Flash version 002
gemini-1.5-flash-001Gemini 1.5 Flash version 001
Embedding Models
ModelDescription
text-embedding-004Latest text embedding model
text-multilingual-embedding-002Multilingual embedding model
textembedding-gecko@003Gecko embedding model
textembedding-gecko-multilingual@001Multilingual Gecko

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Proxy Base URL

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

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Prerequisites

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

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Chat Completions

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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.

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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="")

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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
            }
        )
    )
)

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Google AI Studio vs Vertex AI

FeatureGoogle AI StudioVertex AI
AuthenticationAPI keyGCP credentials (IAM)
Proxy base URLgateway.adaline.ai/v1/googlegateway.adaline.ai/v1/vertex
SDK parameterapi_key="..."vertexai=True, project="...", location="..."
Best forPrototyping, personal projectsEnterprise, production workloads
Data residencyNo controlRegion-specific

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Next Steps

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