The Vertex API for accessing Claude is nearly-identical to the Messages API and supports all of the same options, with two key differences:
  • In Vertex, model is not passed in the request body. Instead, it is specified in the Google Cloud endpoint URL.
  • In Vertex, anthropic_version is passed in the request body (rather than as a header), and must be set to the value vertex-2023-10-16.
Vertex is also supported by Anthropic’s official client SDKs. This guide will walk you through the process of making a request to Claude on Vertex AI in either Python or TypeScript. Note that this guide assumes you have already have a GCP project that is able to use Vertex AI. See using the Claude 3 models from Anthropic for more information on the setup required, as well as a full walkthrough.

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Install an SDK for accessing Vertex AI

First, install Anthropic’s client SDK for your language of choice. 
pip install -U google-cloud-aiplatform "anthropic[vertex]"

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Accessing Vertex AI

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Model Availability

Note that Anthropic model availability varies by region. Search for “Claude” in the Vertex AI Model Garden or go to Use Claude 3 for the latest information.

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API model IDs

ModelVertex AI API model ID
Claude Sonnet 4.5claude-sonnet-4-5@20250929
Claude Sonnet 4claude-sonnet-4@20250514
Claude Sonnet 3.7claude-3-7-sonnet@20250219
Claude Sonnet 3.5 claude-3-5-sonnet-v2@20241022
Claude Opus 4.1claude-opus-4-1@20250805
Claude Opus 4claude-opus-4@20250514
Claude Opus 3 claude-3-opus@20240229
Claude Haiku 3.5claude-3-5-haiku@20241022
Claude Haiku 3claude-3-haiku@20240307

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Making requests

Before running requests you may need to run gcloud auth application-default login to authenticate with GCP. The following examples shows how to generate text from Claude on Vertex AI: 
from anthropic import AnthropicVertex

project_id = "MY_PROJECT_ID"
region = "global"

client = AnthropicVertex(project_id=project_id, region=region)

message = client.messages.create(
    model="claude-sonnet-4-5@20250929",
    max_tokens=100,
    messages=[
        {
            "role": "user",
            "content": "Hey Claude!",
        }
    ],
)
print(message)
See our client SDKs and the official Vertex AI docs for more details.

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Activity logging

Vertex provides a request-response logging service that allows customers to log the prompts and completions associated with your usage. Anthropic recommends that you log your activity on at least a 30-day rolling basis in order to understand your activity and investigate any potential misuse.
Turning on this service does not give Google or Anthropic any access to your content.

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Feature support

You can find all the features currently supported on Vertex here.

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Global vs regional endpoints

Starting with Claude Sonnet 4.5 and all future models, Google Vertex AI offers two endpoint types:
  • Global endpoints: Dynamic routing for maximum availability
  • Regional endpoints: Guaranteed data routing through specific geographic regions
Regional endpoints include a 10% pricing premium over global endpoints.
This applies to Claude Sonnet 4.5 and future models only. Older models (Claude Sonnet 4, Opus 4, and earlier) maintain their existing pricing structures.

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When to use each option

Global endpoints (recommended):
  • Provide maximum availability and uptime
  • Dynamically route requests to regions with available capacity
  • No pricing premium
  • Best for applications where data residency is flexible
  • Only supports pay-as-you-go traffic (provisioned throughput requires regional endpoints)
Regional endpoints:
  • Route traffic through specific geographic regions
  • Required for data residency and compliance requirements
  • Support both pay-as-you-go and provisioned throughput
  • 10% pricing premium reflects infrastructure costs for dedicated regional capacity

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Implementation

Using global endpoints (recommended): Set the region parameter to "global" when initializing the client: 
from anthropic import AnthropicVertex

project_id = "MY_PROJECT_ID"
region = "global"

client = AnthropicVertex(project_id=project_id, region=region)

message = client.messages.create(
    model="claude-sonnet-4-5@20250929",
    max_tokens=100,
    messages=[
        {
            "role": "user",
            "content": "Hey Claude!",
        }
    ],
)
print(message)
Using regional endpoints: Specify a specific region like "us-east1" or "europe-west1": 
from anthropic import AnthropicVertex

project_id = "MY_PROJECT_ID"
region = "us-east1"  # Specify a specific region

client = AnthropicVertex(project_id=project_id, region=region)

message = client.messages.create(
    model="claude-sonnet-4-5@20250929",
    max_tokens=100,
    messages=[
        {
            "role": "user",
            "content": "Hey Claude!",
        }
    ],
)
print(message)

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Additional resources