Claude Code on Google Vertex AI
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Content
Documentation Index
Fetch the complete documentation index at: https://code.claude.com/docs/llms.txt Use this file to discover all available pages before exploring further.
Claude Code on Google Vertex AI
Learn about configuring Claude Code through Google Vertex AI, including setup, IAM configuration, and troubleshooting.
export const ContactSalesCard = ({surface}) => {
const utm = content => utm_source=claude_code&utm_medium=docs&utm_content=${surface}_${content};
const iconArrowRight = (size = 13) => ;
const STYLES = .cc-cs {
--cs-slate: #141413;
--cs-clay: #d97757;
--cs-clay-deep: #c6613f;
--cs-gray-000: #ffffff;
--cs-gray-700: #3d3d3a;
--cs-border-default: rgba(31, 30, 29, 0.15);
font-family: inherit;
}
.dark .cc-cs {
--cs-slate: #f0eee6;
--cs-gray-000: #262624;
--cs-gray-700: #bfbdb4;
--cs-border-default: rgba(240, 238, 230, 0.14);
}
.cc-cs-card {
display: flex; align-items: center; justify-content: space-between;
gap: 16px; padding: 14px 16px; margin: 0;
background: var(--cs-gray-000); border: 0.5px solid var(--cs-border-default);
border-radius: 8px; flex-wrap: wrap;
}
.cc-cs-text { font-size: 13px; color: var(--cs-gray-700); line-height: 1.5; flex: 1; min-width: 240px; }
.cc-cs-text strong { font-weight: 550; color: var(--cs-slate); }
.cc-cs-actions { display: flex; align-items: center; gap: 8px; flex-shrink: 0; }
.cc-cs-btn-clay {
display: inline-flex; align-items: center; gap: 8px;
background: var(--cs-clay-deep); color: #fff; border: none;
border-radius: 8px; padding: 8px 14px;
font-size: 13px; font-weight: 500;
transition: background-color 0.15s; white-space: nowrap;
}
.cc-cs-btn-clay:hover { background: var(--cs-clay); }
.cc-cs-btn-ghost {
display: inline-flex; align-items: center; gap: 8px;
background: transparent; color: var(--cs-gray-700);
border: 0.5px solid var(--cs-border-default);
border-radius: 8px; padding: 8px 14px;
font-size: 13px; font-weight: 500;
}
.cc-cs-btn-ghost:hover { background: rgba(0, 0, 0, 0.04); }
.dark .cc-cs-btn-ghost:hover { background: rgba(255, 255, 255, 0.04); }
@media (max-width: 720px) {
.cc-cs-actions { width: 100%; }
};
return
export const Experiment = ({flag, treatment, children}) => {
const VID_KEY = 'exp_vid';
const CONSENT_COUNTRIES = new Set(['AT', 'BE', 'BG', 'HR', 'CY', 'CZ', 'DK', 'EE', 'FI', 'FR', 'DE', 'GR', 'HU', 'IE', 'IT', 'LV', 'LT', 'LU', 'MT', 'NL', 'PL', 'PT', 'RO', 'SK', 'SI', 'ES', 'SE', 'RE', 'GP', 'MQ', 'GF', 'YT', 'BL', 'MF', 'PM', 'WF', 'PF', 'NC', 'AW', 'CW', 'SX', 'FO', 'GL', 'AX', 'GB', 'UK', 'AI', 'BM', 'IO', 'VG', 'KY', 'FK', 'GI', 'MS', 'PN', 'SH', 'TC', 'GG', 'JE', 'IM', 'CA', 'BR', 'IN']);
const fnv1a = s => {
let h = 0x811c9dc5;
for (let i = 0; i < s.length; i++) {
h ^= s.charCodeAt(i);
h += (h << 1) + (h << 4) + (h << 7) + (h << 8) + (h << 24);
}
return h >>> 0;
};
const bucket = (seed, vid) => fnv1a(fnv1a(seed + vid) + '') % 10000 < 5000 ? 'control' : 'treatment';
const [decision] = useState(() => {
const params = new URLSearchParams(location.search);
const preBucketed = document.documentElement.dataset['gb_' + flag.replace(/-/g, '_')];
const force = params.get('gb-force');
if (force) {
for (const p of force.split(',')) {
const [k, v] = p.split(':');
if (k === flag) return {
variant: v || 'treatment',
track: false
};
}
}
if (navigator.globalPrivacyControl) {
return {
variant: 'control',
track: false
};
}
const prefsMatch = document.cookie.match(/(?:^|; )anthropic-consent-preferences=([^;]+)/);
if (prefsMatch) {
try {
if (JSON.parse(decodeURIComponent(prefsMatch[1])).analytics !== true) {
return {
variant: 'control',
track: false
};
}
} catch {
return {
variant: 'control',
track: false
};
}
} else {
const country = params.get('country')?.toUpperCase() || (document.cookie.match(/(?:^|; )cf_geo=([A-Z]{2})/) || [])[1];
if (!country || CONSENT_COUNTRIES.has(country)) {
return {
variant: 'control',
track: false
};
}
}
let vid;
try {
const ajsMatch = document.cookie.match(/(?:^|; )ajs_anonymous_id=([^;]+)/);
if (ajsMatch) {
vid = decodeURIComponent(ajsMatch[1]).replace(/^"|"$/g, '');
} else {
vid = localStorage.getItem(VID_KEY);
if (!vid) {
vid = crypto.randomUUID();
}
document.cookie = ajs_anonymous_id=${vid}; domain=.claude.com; path=/; Secure; SameSite=Lax; max-age=31536000;
}
try {
localStorage.setItem(VID_KEY, vid);
} catch {}
} catch {
return {
variant: 'control',
track: false
};
}
const variant = preBucketed === '1' ? 'treatment' : preBucketed === '0' ? 'control' : bucket(flag, vid);
return {
variant,
track: true,
vid
};
});
useEffect(() => {
if (!decision.track) return;
fetch('https://api.anthropic.com/api/event_logging/v2/batch', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'x-service-name': 'claude_code_docs'
},
body: JSON.stringify({
events: [{
event_type: 'GrowthbookExperimentEvent',
event_data: {
device_id: decision.vid,
anonymous_id: decision.vid,
timestamp: new Date().toISOString(),
experiment_id: flag,
variation_id: decision.variant === 'treatment' ? 1 : 0,
environment: 'production'
}
}]
}),
keepalive: true
}).catch(() => {});
}, []);
return decision.variant === 'treatment' ? treatment : children;
};
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.
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.
claude --version to check.
claude. At the login prompt, select 3rd-party platform, then Google Vertex AI.
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.
Region configuration
Claude Code can be used with both Vertex AI global and regional endpoints.
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.
1. Enable Vertex AI API
Enable the Vertex AI API in your GCP project:
```bash theme={null}
Set your project ID
gcloud config set project YOUR-PROJECT-ID
Enable Vertex AI API
gcloud services enable aiplatform.googleapis.com
### 2. Request model access
Request access to Claude models in Vertex AI:
1. Navigate to the [Vertex AI Model Garden](https://console.cloud.google.com/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)
### 3. Configure GCP credentials
Claude Code uses standard Google Cloud authentication.
For more information, see [Google Cloud authentication documentation](https://cloud.google.com/docs/authentication).
<Note>
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`.
</Note>
### 4. Configure Claude Code
Set the following environment variables:
```bash theme={null}
# 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.
5. Pin model versions
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:
```bash theme={null} 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](https://platform.claude.com/docs/en/about-claude/models/overview). See [Model configuration](https://code.claude.com/en/model-config#pin-models-for-third-party-deployments) for the full list of environment variables.
Claude Code uses these default models when no pinning variables are set:
| Model type | Default value |
| :--------------- | :--------------------------- |
| Primary model | `claude-sonnet-4-5@20250929` |
| Small/fast model | `claude-haiku-4-5@20251001` |
To customize models further:
```bash theme={null}
export ANTHROPIC_MODEL='claude-opus-4-7'
export ANTHROPIC_DEFAULT_HAIKU_MODEL='claude-haiku-4-5@20251001'
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.
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.
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.
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_MODELorANTHROPIC_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=globalfor better availability