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Prerequisites

Before configuring Claude Code with Vertex AI, ensure you have:
  • A Google Cloud Platform (GCP) account with billing enabled
  • A GCP project with Vertex AI API enabled
  • Access to desired Claude models (for example, Claude Sonnet 4.6)
  • Google Cloud SDK (gcloud) installed and configured
  • Quota allocated in desired GCP region
To sign in with your own Vertex AI credentials, follow Sign in with Vertex AI below. To deploy Claude Code across a team, use the manual setup steps and pin your model versions before rolling out.

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Sign in with Vertex AI

If you have Google Cloud credentials and want to start using Claude Code through Vertex AI, the login wizard walks you through it. You complete the GCP-side prerequisites once per project; the wizard handles the Claude Code side.
The Vertex AI setup wizard requires Claude Code v2.1.98 or later. Run claude --version to check.
1

Enable Claude models in your GCP project

Enable the Vertex AI API for your project, then request access to the Claude models you want in the Vertex AI Model Garden. See IAM configuration for the permissions your account needs.
2

Start Claude Code and choose Vertex AI

Run claude. At the login prompt, select 3rd-party platform, then Google Vertex AI.
3

Follow the wizard prompts

Choose how you authenticate to Google Cloud: Application Default Credentials from gcloud, a service account key file, or credentials already in your environment. The wizard detects your project and region, verifies which Claude models your project can invoke, and lets you pin them. It saves the result to the env block of your user settings file, so you don’t need to export environment variables yourself.
After you’ve signed in, run /setup-vertex any time to reopen the wizard and change your credentials, project, region, or model pins.

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Region configuration

Claude Code can be used with both Vertex AI global and regional endpoints.
Vertex AI may not support the Claude Code default models in all regions or on global endpoints. You may need to switch to a supported region, use a regional endpoint, or specify a supported model.

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Set up manually

To configure Vertex AI through environment variables instead of the wizard, for example in CI or a scripted enterprise rollout, follow the steps below.

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1. Enable Vertex AI API

Enable the Vertex AI API in your GCP project: 
# Set your project ID
gcloud config set project YOUR-PROJECT-ID

# Enable Vertex AI API
gcloud services enable aiplatform.googleapis.com

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2. Request model access

Request access to Claude models in Vertex AI:
  1. Navigate to the Vertex AI Model Garden
  2. Search for “Claude” models
  3. Request access to desired Claude models (for example, Claude Sonnet 4.6)
  4. Wait for approval (may take 24-48 hours)

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3. Configure GCP credentials

Claude Code uses standard Google Cloud authentication. For more information, see Google Cloud authentication documentation.
When authenticating, Claude Code will automatically use the project ID from the ANTHROPIC_VERTEX_PROJECT_ID environment variable. To override this, set one of these environment variables: GCLOUD_PROJECT, GOOGLE_CLOUD_PROJECT, or GOOGLE_APPLICATION_CREDENTIALS.

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4. Configure Claude Code

Set the following environment variables: 
# Enable Vertex AI integration
export CLAUDE_CODE_USE_VERTEX=1
export CLOUD_ML_REGION=global
export ANTHROPIC_VERTEX_PROJECT_ID=YOUR-PROJECT-ID

# Optional: Override the Vertex endpoint URL for custom endpoints or gateways
# export ANTHROPIC_VERTEX_BASE_URL=https://aiplatform.googleapis.com

# Optional: Disable prompt caching if needed
export DISABLE_PROMPT_CACHING=1

# When CLOUD_ML_REGION=global, override region for models that don't support global endpoints
export VERTEX_REGION_CLAUDE_HAIKU_4_5=us-east5
export VERTEX_REGION_CLAUDE_4_6_SONNET=europe-west1
Most model versions have a corresponding VERTEX_REGION_CLAUDE_* variable. See the Environment variables reference for the full list. Check Vertex Model Garden to determine which models support global endpoints versus regional only. Prompt caching is automatically supported when you specify the cache_control ephemeral flag. To disable it, set DISABLE_PROMPT_CACHING=1. For heightened rate limits, contact Google Cloud support. When using Vertex AI, the /login and /logout commands are disabled since authentication is handled through Google Cloud credentials.

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5. Pin model versions

Pin specific model versions when deploying to multiple users. Without pinning, model aliases such as sonnet and opus resolve to the latest version, which may not yet be enabled in your Vertex AI project when Anthropic releases an update. Claude Code falls back to the previous version at startup when the latest is unavailable, but pinning lets you control when your users move to a new model.
Set these environment variables to specific Vertex AI model IDs. Without ANTHROPIC_DEFAULT_OPUS_MODEL, the opus alias on Vertex resolves to Opus 4.6. Set it to the Opus 4.7 ID to use the latest model: 
export ANTHROPIC_DEFAULT_OPUS_MODEL='claude-opus-4-7'
export ANTHROPIC_DEFAULT_SONNET_MODEL='claude-sonnet-4-6'
export ANTHROPIC_DEFAULT_HAIKU_MODEL='claude-haiku-4-5@20251001'
For current and legacy model IDs, see Models overview. See Model configuration for the full list of environment variables. Claude Code uses these default models when no pinning variables are set:
Model typeDefault value
Primary modelclaude-sonnet-4-5@20250929
Small/fast modelclaude-haiku-4-5@20251001
To customize models further: 
export ANTHROPIC_MODEL='claude-opus-4-7'
export ANTHROPIC_DEFAULT_HAIKU_MODEL='claude-haiku-4-5@20251001'

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Startup model checks

When Claude Code starts with Vertex AI configured, it verifies that the models it intends to use are accessible in your project. This check requires Claude Code v2.1.98 or later. If you have pinned a model version that is older than the current Claude Code default, and your project can invoke the newer version, Claude Code prompts you to update the pin. Accepting writes the new model ID to your user settings file and restarts Claude Code. Declining is remembered until the next default version change. If you have not pinned a model and the current default is unavailable in your project, Claude Code falls back to the previous version for the current session and shows a notice. The fallback is not persisted. Enable the newer model in Model Garden or pin a version to make the choice permanent.

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IAM configuration

Assign the required IAM permissions: The roles/aiplatform.user role includes the required permissions:
  • aiplatform.endpoints.predict - Required for model invocation and token counting
For more restrictive permissions, create a custom role with only the permissions above. For details, see Vertex IAM documentation.
Create a dedicated GCP project for Claude Code to simplify cost tracking and access control.

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1M token context window

Claude Opus 4.7, Opus 4.6, and Sonnet 4.6 support the 1M token context window on Vertex AI. Claude Code automatically enables the extended context window when you select a 1M model variant. The setup wizard offers a 1M context option when it pins models. To enable it for a manually pinned model instead, append [1m] to the model ID. See Pin models for third-party deployments for details.

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Troubleshooting

If you encounter quota issues:
  • Check current quotas or request quota increase through Cloud Console
If you encounter “model not found” 404 errors:
  • Confirm model is Enabled in Model Garden
  • Verify you have access to the specified region
  • If using CLOUD_ML_REGION=global, check that your models support global endpoints in Model Garden under “Supported features”. For models that don’t support global endpoints, either:
    • Specify a supported model via ANTHROPIC_MODEL or ANTHROPIC_DEFAULT_HAIKU_MODEL, or
    • Set a regional endpoint using VERTEX_REGION_<MODEL_NAME> environment variables
If you encounter 429 errors:
  • For regional endpoints, ensure the primary model and small/fast model are supported in your selected region
  • Consider switching to CLOUD_ML_REGION=global for better availability

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Additional resources