Claude on Google Vertex AI: GCP Integration Guide

Why Claude on Vertex AI?

If your infrastructure runs on Google Cloud, Vertex AI is the most direct path to Claude.

The practical reason teams end up on Vertex AI instead of the direct Anthropic API, visible in the ongoing threads on r/googlecloud and r/devops: it's almost never a technical preference. It's procurement. If your org already has a negotiated GCP commit, a VPC-SC perimeter, and a centralized billing process, adding another SaaS vendor with its own contract, its own DPA, and its own invoicing is a 3-month fight — whereas enabling Claude in Model Garden is three clicks. The Vertex AI Model Garden page for Claude exists exactly because this friction is real and widespread.

Where the community correctly pushes back: "it's the same model" is almost but not quite true. Model availability lags the direct API (new Claude versions land on Anthropic first, then Bedrock and Vertex), some features like extended thinking or fine-grained safety controls can arrive later, and quota/region footprints are different. If you're building a product that must ship on the day a new Claude version releases, Vertex is the wrong plane of abstraction; if you're building an enterprise internal tool, it's exactly the right one.

Pragmatic heuristic: choose the direct API for greenfield experimentation and anything latency-sensitive, choose Vertex (or Bedrock) when IT, legal, or finance would otherwise block the rollout.

Setup: Step by Step

1. Enable Claude in the Model Garden

1. →Go to the GCP console > Vertex AI > Model Garden
2. →Search for "Claude" in the catalog
3. →Click Enable for the desired models
4. →Accept the Anthropic terms of use

2. Configure a Service Account

# Create a service account
gcloud iam service-accounts create claude-vertex \
    --display-name="Claude Vertex AI"

# Assign the Vertex AI User role
gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
    --member="serviceAccount:claude-vertex@YOUR_PROJECT_ID.iam.gserviceaccount.com" \
    --role="roles/aiplatform.user"

# Generate a key (for local development)
gcloud iam service-accounts keys create key.json \
    --iam-account=claude-vertex@YOUR_PROJECT_ID.iam.gserviceaccount.com

3. Install the SDKs

pip install anthropic[vertex] google-auth

Code Examples

Basic Call with the Anthropic SDK

import anthropic

client = anthropic.AnthropicVertex(
    project_id="your-gcp-project-id",
    region="us-east5"
)

message = client.messages.create(
    model="claude-sonnet-4@20250514",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Explain the benefits of BigQuery for data analysis."}
    ]
)

print(message.content[0].text)

With System Prompt

message = client.messages.create(
    model="claude-sonnet-4@20250514",
    max_tokens=2048,
    system="You are an expert GCP cloud architect. Recommend Google Cloud solutions.",
    messages=[
        {"role": "user", "content": "I need an architecture for a real-time data pipeline."}
    ]
)

Streaming

with client.messages.stream(
    model="claude-sonnet-4@20250514",
    max_tokens=2048,
    messages=[{"role": "user", "content": "Write a GKE migration guide."}]
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Authentication on GCP (no JSON key)

# Automatic authentication via Application Default Credentials
# Works on GCE, Cloud Run, GKE, Cloud Functions
client = anthropic.AnthropicVertex(
    project_id="your-project-id",
    region="us-east5"
    # No explicit credentials needed!
)

Pricing: Vertex AI vs Direct API

Vertex pricing advantages:

• →Committed Use Discounts (CUDs): Volume commitment discounts
• →Per-second billing: No minimum
• →GCP credits: Usable for Claude (including free startup credits)
• →Batch predictions: Reduction for batch processing

BigQuery Integration

A powerful use case: using Claude to analyze BigQuery data.

from google.cloud import bigquery
import anthropic

# Retrieve BigQuery data
bq_client = bigquery.Client()
query = """
    SELECT product_name, SUM(revenue) as total_revenue, COUNT(*) as orders
    FROM `project.dataset.sales`
    WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
    GROUP BY product_name
    ORDER BY total_revenue DESC
    LIMIT 20
"""
results = bq_client.query(query).to_dataframe()

# Analyze with Claude
vertex_client = anthropic.AnthropicVertex(
    project_id="your-project-id",
    region="us-east5"
)

message = vertex_client.messages.create(
    model="claude-sonnet-4@20250514",
    max_tokens=2048,
    messages=[{
        "role": "user",
        "content": f"""Analyze this sales data from the last 30 days:

{results.to_markdown()}

Provide:
1. The top 3 performing products and why
2. Notable trends
3. Recommendations for next month"""
    }]
)

print(message.content[0].text)

Enterprise Architecture on GCP

Typical Architecture

Cloud Run + Claude

# app.py - Cloud Run service with Claude
from flask import Flask, request, jsonify
import anthropic

app = Flask(__name__)
client = anthropic.AnthropicVertex(
    project_id="your-project-id",
    region="us-east5"
)

@app.route("/analyze", methods=["POST"])
def analyze():
    data = request.json
    
    message = client.messages.create(
        model="claude-sonnet-4@20250514",
        max_tokens=2048,
        messages=[{"role": "user", "content": data["question"]}]
    )
    
    return jsonify({
        "answer": message.content[0].text,
        "model": message.model,
        "tokens": {
            "input": message.usage.input_tokens,
            "output": message.usage.output_tokens
        }
    })

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8080)

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY app.py .
CMD ["python", "app.py"]

# Deploy to Cloud Run
gcloud run deploy claude-service \
    --source . \
    --region us-east5 \
    --service-account claude-vertex@YOUR_PROJECT.iam.gserviceaccount.com

Bedrock vs Vertex AI Comparison

Available Regions

Recommendation: Use europe-west1 for European applications (minimal latency + easier GDPR compliance).

Migrating from the Direct API