Anthropic’s Claude models are now generally available through Vertex AI.

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.

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]"

npm install @anthropic-ai/vertex-sdk

Accessing Vertex AI

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.

API model names

Model	Vertex AI API model name
Claude 3 Haiku	`claude-3-haiku@20240307`
Claude 3 Sonnet	`claude-3-sonnet@20240229`
Claude 3 Opus (Public Preview)	`claude-3-opus@20240229`

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 3 Haiku on Vertex AI:

from anthropic import AnthropicVertex

project_id = "MY_PROJECT_ID"
# Where the model is running. e.g. us-central1 or europe-west4 for haiku
region = "MY_REGION"

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

message = client.messages.create(
    model="claude-3-haiku@20240307",
    max_tokens=100,
    messages=[
        {
            "role": "user",
            "content": "Hey Claude!",
        }
    ],
)
print(message)

import { AnthropicVertex } from '@anthropic-ai/vertex-sdk';


const projectId = 'MY_PROJECT_ID';
# Where the model is running. e.g. us-central1 or europe-west4 for haiku
const region = 'MY_REGION';

// Goes through the standard `google-auth-library` flow.
const client = new AnthropicVertex({
  projectId,
  region,
});
);

async function main() {
  const result = await client.messages.create({
    model: 'claude-3-haiku@20240307',
    max_tokens: 100,
    messages: [
      {
        role: 'user',
        content: 'Hey Claude!',
      },
    ],
  });
  console.log(JSON.stringify(result, null, 2));
}

main();

MODEL_ID=claude-3-haiku@20240307
REGION=us-central1
PROJECT_ID=MY_PROJECT_ID

curl \
-X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json" \
https://$LOCATION-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/publishers/anthropic/models/${MODEL_ID}:streamRawPredict -d \
'{
  "anthropic_version": "vertex-2023-10-16",
  "messages": [{
    "role": "user",
    "content": "Hey Claude!"
  }],
  "max_tokens": 100,
}'

See our client SDKs and the official Vertex AI docs for more details.