claude-code-sandbox/docs/claude-code-docs/Bedrock, Vertex, and proxies.md

title	source	author	published	created	description	tags
Bedrock, Vertex, and proxies	https://docs.anthropic.com/en/docs/claude-code/bedrock-vertex-proxies	Anthropic		2025-05-25	Configure Claude Code to work with Amazon Bedrock and Google Vertex AI, and connect through proxies.	clippings

Model configuration

Claude Code uses the following defaults:

Provider	Default Model
Anthropic Console	claude-sonnet-4-20250514
Claude Max	claude-opus-4-20250514 or claude-sonnet-4-20250514 based on Max usage limits
Amazon Bedrock	claude-3-7-sonnet-20250219
Google Vertex AI	claude-sonnet-4-20250514

The default values can be overridden in several ways based on the following precedence from top to bottom:

• --model CLI flag. Applies within the session only.
• ANTHROPIC_MODEL environment variable. Applies within the session only.
• User Settings ~/.claude/settings.json: Persists across sessions.

You can supply a full model name, the alias sonnet for the latest Claude Sonnet model for your provider, or the alias opus for the latest Claude Opus model for your provider.

See our model names reference for all available models across different providers.

Use with third-party APIs

Claude Code requires access to both Claude Sonnet 3.7 and Claude Haiku 3.5 models, regardless of which API provider you use.

Connect to Amazon Bedrock

CLAUDE_CODE_USE_BEDROCK=1

If you don’t have prompt caching enabled, also set:

DISABLE_PROMPT_CACHING=1

Contact Amazon Bedrock for prompt caching for reduced costs and higher rate limits.

Requires standard AWS SDK credentials (e.g., ~/.aws/credentials or relevant environment variables like AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY). To set up AWS credentials, run:

If you’d like to access Claude Code via proxy, you can use the ANTHROPIC_BEDROCK_BASE_URL environment variable:

ANTHROPIC_BEDROCK_BASE_URL='https://your-proxy-url'

If your proxy maintains its own AWS credentials, you can use the CLAUDE_CODE_SKIP_BEDROCK_AUTH environment variable to remove Claude Code’s requirement for AWS credentials.

CLAUDE_CODE_SKIP_BEDROCK_AUTH=1

Users will need access to both Claude Sonnet 3.7 and Claude Haiku 3.5 models in their AWS account. If you have a model access role, you may need to request access to these models if they’re not already available. Access to Bedrock in each region is necessary because inference profiles require cross-region capability.

Connect to Google Vertex AI

If you don’t have prompt caching enabled, also set:

DISABLE_PROMPT_CACHING=1

Claude Code on Vertex AI currently only supports the us-east5 region. Make sure your project has quota allocated in this specific region.

Users will need access to both Claude Sonnet 3.7 and Claude Haiku 3.5 models in their Vertex AI project.

Requires standard GCP credentials configured through google-auth-library. To set up GCP credentials, run:

If you’d like to access Claude Code via proxy, you can use the ANTHROPIC_VERTEX_BASE_URL environment variable:

ANTHROPIC_VERTEX_BASE_URL='https://your-proxy-url'

If your proxy maintains its own GCP credentials, you can use the CLAUDE_CODE_SKIP_VERTEX_AUTH environment variable to remove Claude Code’s requirement for GCP credentials.

CLAUDE_CODE_SKIP_VERTEX_AUTH=1

For the best experience, contact Google for heightened rate limits.

Connect through a proxy

When using Claude Code with an LLM proxy, you can control authentication behavior using the following environment variables and configs. Note that you can mix and match these with Bedrock and Vertex-specific settings.

Settings

Claude Code supports a number of settings controlled via environment variables to configure usage with Bedrock and Vertex. See Environment variables for a complete reference.

If you prefer to configure via a file instead of environment variables, you can add any of these settings to the env object in your Claude Code settings files.

You can also configure the apiKeyHelper setting, to set a custom shell script to get an API key (invoked once at startup, and cached for the duration of each session, or until CLAUDE_CODE_API_KEY_HELPER_TTL_MS elapses).

LiteLLM

LiteLLM is a third-party proxy service. Anthropic doesn’t endorse, maintain, or audit LiteLLM’s security or functionality. This guide is provided for informational purposes and may become outdated. Use at your own discretion.

This section shows configuration of Claude Code with LiteLLM Proxy Server, a third-party LLM proxy which offers usage and spend tracking, centralized authentication, per-user budgeting, and more.

Step 1: Prerequisites

• Claude Code updated to the latest version
• LiteLLM Proxy Server running and network-accessible to Claude Code
• Your LiteLLM proxy key

Step 2: Set up proxy authentication

Choose one of these authentication methods:

Option A: Static proxy key Set your proxy key as an environment variable:

ANTHROPIC_AUTH_TOKEN=your-proxy-key

Option B: Dynamic proxy key If your organization uses rotating keys or dynamic authentication:

1. Do not set the ANTHROPIC_AUTH_TOKEN environment variable
2. Author a key helper script to provide authentication tokens
3. Register the script under apiKeyHelper configuration in your Claude Code settings
4. Set the token lifetime to enable automatic refresh:

   CLAUDE_CODE_API_KEY_HELPER_TTL_MS=3600000
   

   Set this to the lifetime (in milliseconds) of tokens returned by your apiKeyHelper.

Step 3: Configure your deployment

Choose which Claude deployment you want to use through LiteLLM:

• Anthropic API: Direct connection to Anthropic’s API
• Bedrock: Amazon Bedrock with Claude models
• Vertex AI: Google Cloud Vertex AI with Claude models

Option A: Anthropic API through LiteLLM

1. Configure the LiteLLM endpoint:

   ANTHROPIC_BASE_URL=https://litellm-url:4000/anthropic
   

Option B: Bedrock through LiteLLM

1. Configure Bedrock settings:

Option C: Vertex AI through LiteLLM

Recommended: Proxy-specified credentials

1. Configure Vertex settings:

Alternative: Client-specified credentials

If you prefer to use local GCP credentials:

1. Authenticate with GCP locally:
2. Configure Vertex settings:
3. Update LiteLLM header configuration: Ensure your LiteLLM config has general_settings.litellm_key_header_name set to Proxy-Authorization, since the pass-through GCP token will be located on the Authorization header.

Step 4. Selecting a model

By default, the models will use those specified in Model configuration.

If you have configured custom model names in LiteLLM, set the aforementioned environment variables to those custom names.

For more detailed information, refer to the LiteLLM documentation.