gh-obra-superpowers-developing-for-claude-code/skills/working-with-claude-code/references/third-party-integrations.md

# Enterprise deployment overview

> Learn how Claude Code can integrate with various third-party services and infrastructure to meet enterprise deployment requirements.

This page provides an overview of available deployment options and helps you choose the right configuration for your organization.

## Provider comparison

<table>
  <thead>
    <tr>
      <th>Feature</th>
      <th>Anthropic</th>
      <th>Amazon Bedrock</th>
      <th>Google Vertex AI</th>
    </tr>
  </thead>

  <tbody>
    <tr>
      <td>Regions</td>
      <td>Supported [countries](https://www.anthropic.com/supported-countries)</td>
      <td>Multiple AWS [regions](https://docs.aws.amazon.com/bedrock/latest/userguide/models-regions.html)</td>
      <td>Multiple GCP [regions](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/locations)</td>
    </tr>

    <tr>
      <td>Prompt caching</td>
      <td>Enabled by default</td>
      <td>Enabled by default</td>
      <td>Enabled by default</td>
    </tr>

    <tr>
      <td>Authentication</td>
      <td>API key</td>
      <td>AWS credentials (IAM)</td>
      <td>GCP credentials (OAuth/Service Account)</td>
    </tr>

    <tr>
      <td>Cost tracking</td>
      <td>Dashboard</td>
      <td>AWS Cost Explorer</td>
      <td>GCP Billing</td>
    </tr>

    <tr>
      <td>Enterprise features</td>
      <td>Teams, usage monitoring</td>
      <td>IAM policies, CloudTrail</td>
      <td>IAM roles, Cloud Audit Logs</td>
    </tr>
  </tbody>
</table>

## Cloud providers

<CardGroup cols={2}>
  <Card title="Amazon Bedrock" icon="aws" href="/en/docs/claude-code/amazon-bedrock">
    Use Claude models through AWS infrastructure with IAM-based authentication and AWS-native monitoring
  </Card>

  <Card title="Google Vertex AI" icon="google" href="/en/docs/claude-code/google-vertex-ai">
    Access Claude models via Google Cloud Platform with enterprise-grade security and compliance
  </Card>
</CardGroup>

## Corporate infrastructure

<CardGroup cols={2}>
  <Card title="Enterprise Network" icon="shield" href="/en/docs/claude-code/network-config">
    Configure Claude Code to work with your organization's proxy servers and SSL/TLS requirements
  </Card>

  <Card title="LLM Gateway" icon="server" href="/en/docs/claude-code/llm-gateway">
    Deploy centralized model access with usage tracking, budgeting, and audit logging
  </Card>
</CardGroup>

## Configuration overview

Claude Code supports flexible configuration options that allow you to combine different providers and infrastructure:

<Note>
  Understand the difference between:

  * **Corporate proxy**: An HTTP/HTTPS proxy for routing traffic (set via `HTTPS_PROXY` or `HTTP_PROXY`)
  * **LLM Gateway**: A service that handles authentication and provides provider-compatible endpoints (set via `ANTHROPIC_BASE_URL`, `ANTHROPIC_BEDROCK_BASE_URL`, or `ANTHROPIC_VERTEX_BASE_URL`)

  Both configurations can be used in tandem.
</Note>

### Using Bedrock with corporate proxy

Route Bedrock traffic through a corporate HTTP/HTTPS proxy:

```bash  theme={null}
# Enable Bedrock
export CLAUDE_CODE_USE_BEDROCK=1
export AWS_REGION=us-east-1

# Configure corporate proxy
export HTTPS_PROXY='https://proxy.example.com:8080'
```

### Using Bedrock with LLM Gateway

Use a gateway service that provides Bedrock-compatible endpoints:

```bash  theme={null}
# Enable Bedrock
export CLAUDE_CODE_USE_BEDROCK=1

# Configure LLM gateway
export ANTHROPIC_BEDROCK_BASE_URL='https://your-llm-gateway.com/bedrock'
export CLAUDE_CODE_SKIP_BEDROCK_AUTH=1  # If gateway handles AWS auth
```

### Using Vertex AI with corporate proxy

Route Vertex AI traffic through a corporate HTTP/HTTPS proxy:

```bash  theme={null}
# Enable Vertex
export CLAUDE_CODE_USE_VERTEX=1
export CLOUD_ML_REGION=us-east5
export ANTHROPIC_VERTEX_PROJECT_ID=your-project-id

# Configure corporate proxy
export HTTPS_PROXY='https://proxy.example.com:8080'
```

### Using Vertex AI with LLM Gateway

Combine Google Vertex AI models with an LLM gateway for centralized management:

```bash  theme={null}
# Enable Vertex
export CLAUDE_CODE_USE_VERTEX=1

# Configure LLM gateway
export ANTHROPIC_VERTEX_BASE_URL='https://your-llm-gateway.com/vertex'
export CLAUDE_CODE_SKIP_VERTEX_AUTH=1  # If gateway handles GCP auth
```

### Authentication configuration

Claude Code uses the `ANTHROPIC_AUTH_TOKEN` for the `Authorization` header when needed. The `SKIP_AUTH` flags (`CLAUDE_CODE_SKIP_BEDROCK_AUTH`, `CLAUDE_CODE_SKIP_VERTEX_AUTH`) are used in LLM gateway scenarios where the gateway handles provider authentication.

## Choosing the right deployment configuration

Consider these factors when selecting your deployment approach:

### Direct provider access

Best for organizations that:

* Want the simplest setup
* Have existing AWS or GCP infrastructure
* Need provider-native monitoring and compliance

### Corporate proxy

Best for organizations that:

* Have existing corporate proxy requirements
* Need traffic monitoring and compliance
* Must route all traffic through specific network paths

### LLM Gateway

Best for organizations that:

* Need usage tracking across teams
* Want to dynamically switch between models
* Require custom rate limiting or budgets
* Need centralized authentication management

## Debugging

When debugging your deployment:

* Use the `claude /status` [slash command](/en/docs/claude-code/slash-commands). This command provides observability into any applied authentication, proxy, and URL settings.
* Set environment variable `export ANTHROPIC_LOG=debug` to log requests.

## Best practices for organizations

### 1. Invest in documentation and memory

We strongly recommend investing in documentation so that Claude Code understands your codebase. Organizations can deploy CLAUDE.md files at multiple levels:

* **Organization-wide**: Deploy to system directories like `/Library/Application Support/ClaudeCode/CLAUDE.md` (macOS) for company-wide standards
* **Repository-level**: Create `CLAUDE.md` files in repository roots containing project architecture, build commands, and contribution guidelines. Check these into source control so all users benefit

  [Learn more](/en/docs/claude-code/memory).

### 2. Simplify deployment

If you have a custom development environment, we find that creating a "one click" way to install Claude Code is key to growing adoption across an organization.

### 3. Start with guided usage

Encourage new users to try Claude Code for codebase Q\&A, or on smaller bug fixes or feature requests. Ask Claude Code to make a plan. Check Claude's suggestions and give feedback if it's off-track. Over time, as users understand this new paradigm better, then they'll be more effective at letting Claude Code run more agentically.

### 4. Configure security policies

Security teams can configure managed permissions for what Claude Code is and is not allowed to do, which cannot be overwritten by local configuration. [Learn more](/en/docs/claude-code/security).

### 5. Leverage MCP for integrations

MCP is a great way to give Claude Code more information, such as connecting to ticket management systems or error logs. We recommend that one central team configures MCP servers and checks a `.mcp.json` configuration into the codebase so that all users benefit. [Learn more](/en/docs/claude-code/mcp).

At Anthropic, we trust Claude Code to power development across every Anthropic codebase. We hope you enjoy using Claude Code as much as we do!

## Next steps

* [Set up Amazon Bedrock](/en/docs/claude-code/amazon-bedrock) for AWS-native deployment
* [Configure Google Vertex AI](/en/docs/claude-code/google-vertex-ai) for GCP deployment
* [Configure Enterprise Network](/en/docs/claude-code/network-config) for network requirements
* [Deploy LLM Gateway](/en/docs/claude-code/llm-gateway) for enterprise management
* [Settings](/en/docs/claude-code/settings) for configuration options and environment variables