Route Claude Code through Google Vertex AI via Portkey. Get full observability, cost tracking, and budget controls with your existing GCP infrastructure. See Claude Code overview for why platform teams use Portkey.

Important: Always use the latest version of Claude Code. Older versions may not work with Portkey’s gateway.

Quick Start

Add Vertex AI Provider in Portkey

Go to AI Providers → Add Provider → Select Google Vertex AI.Enter your GCP credentials:

Service Account JSON key
GCP Project ID
Region (e.g., us-central1)

Create a slug like vertex-prod.

Get Your Portkey API Key

Go to API Keys → Generate a new key.

Configure Claude Code

Edit ~/.claude/settings.json (user-level) or .claude/settings.json (project-level):

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.portkey.ai",
    "ANTHROPIC_AUTH_TOKEN": "YOUR_PORTKEY_API_KEY",
    "ANTHROPIC_CUSTOM_HEADERS": "x-portkey-api-key: YOUR_PORTKEY_API_KEY\nx-portkey-provider: @vertex-prod",
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "claude-sonnet-4-20250514",
    "ANTHROPIC_DEFAULT_OPUS_MODEL": "claude-opus-4-20250514",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL": "claude-haiku-4-20250514"
  },
  "model": "claude-sonnet-4-20250514"
}

Replace:

YOUR_PORTKEY_API_KEY with your Portkey API key
@vertex-prod with your provider slug

Model names are required. Without these settings, Claude Code will use Anthropic Direct API model names which may fail on Vertex AI.

That’s it! All Claude Code requests now route through Vertex AI via Portkey. Monitor usage in the Portkey Dashboard.

Forward Headers (Required for Some Features)

Some Claude Code features require the anthropic-beta header to reach Vertex AI. Configure this in a Portkey Config:

Create a Config

Go to Configs → Create Config:

{
  "provider": "@vertex-prod",
  "forward_headers": ["anthropic-beta"]
}

Save and copy the Config ID.

Update Claude Code Settings

Add the config to your settings:

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.portkey.ai",
    "ANTHROPIC_AUTH_TOKEN": "YOUR_PORTKEY_API_KEY",
    "ANTHROPIC_CUSTOM_HEADERS": "x-portkey-api-key: YOUR_PORTKEY_API_KEY\nx-portkey-config: YOUR_CONFIG_ID"
  }
}

Trace Requests

Add trace IDs to group and debug requests in the Portkey Dashboard:

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.portkey.ai",
    "ANTHROPIC_AUTH_TOKEN": "YOUR_PORTKEY_API_KEY",
    "ANTHROPIC_CUSTOM_HEADERS": "x-portkey-api-key: YOUR_PORTKEY_API_KEY\nx-portkey-provider: @vertex-prod\nx-portkey-trace-id: claude-code-session-123"
  }
}

Use trace IDs to:

Group all requests from a coding session
Debug issues by filtering logs
Track usage per project or task

Advanced Configuration

Fallbacks

Route to backup providers when Vertex AI fails. Create a config with fallback targets:

{
  "strategy": { "mode": "fallback" },
  "targets": [
    { "provider": "@vertex-prod" },
    { "provider": "@anthropic-prod" },
    { "provider": "@bedrock-prod" }
  ],
  "forward_headers": ["anthropic-beta"]
}

Load Balancing

Distribute requests across multiple regions:

{
  "strategy": { "mode": "loadbalance" },
  "targets": [
    { "provider": "@vertex-us-central", "weight": 0.5 },
    { "provider": "@vertex-us-east", "weight": 0.5 }
  ],
  "forward_headers": ["anthropic-beta"]
}

Caching

Reduce costs and latency for repeated queries:

{
  "provider": "@vertex-prod",
  "cache": { "mode": "simple" },
  "forward_headers": ["anthropic-beta"]
}

Retries

Automatically retry failed requests:

{
  "provider": "@vertex-prod",
  "retry": { "attempts": 3, "on_status_codes": [429, 500, 502, 503] },
  "forward_headers": ["anthropic-beta"]
}

Budget Limits

Set spending controls at the provider level:

Go to AI Providers → Select your Vertex AI provider
Click Budget & Limits
Configure:
- Cost limit: Maximum spend (e.g., $500/month)
- Token limit: Maximum tokens (e.g., 10M tokens/week)
- Rate limit: Requests per minute/hour

Budget limits prevent runaway costs from agentic coding sessions.

Troubleshooting

Error: `API Error: 500 Message: fetch failed`

Cause: Using the wrong base URL or environment variables. Fix: Ensure you’re using:

ANTHROPIC_BASE_URL (not ANTHROPIC_VERTEX_BASE_URL)
https://api.portkey.ai (not https://api.portkey.ai/v1)

❌ Wrong:

{
  "env": {
    "ANTHROPIC_VERTEX_BASE_URL": "https://api.portkey.ai/v1",
    "CLAUDE_CODE_USE_VERTEX": "1"
  }
}

✅ Correct:

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.portkey.ai",
    "ANTHROPIC_AUTH_TOKEN": "YOUR_PORTKEY_API_KEY",
    "ANTHROPIC_CUSTOM_HEADERS": "x-portkey-api-key: YOUR_PORTKEY_API_KEY\nx-portkey-provider: @vertex-prod"
  }
}

Lightning Symbol (⚡) in Logs

Cause: Requests are going through as passthrough, meaning Portkey isn’t handling them properly. Fix:

Remove /v1 from the base URL — use https://api.portkey.ai
Ensure x-portkey-provider or x-portkey-config is set in headers
Verify your Portkey API key is correct

Claude Code Version Issues

Cause: Older versions of Claude Code may not be compatible. Fix: Update to the latest version:

claude update

Or reinstall:

curl -fsSL https://claude.ai/install.sh | bash

Complete Example

Full configuration with all features enabled:

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.portkey.ai",
    "ANTHROPIC_AUTH_TOKEN": "YOUR_PORTKEY_API_KEY",
    "ANTHROPIC_CUSTOM_HEADERS": "x-portkey-api-key: YOUR_PORTKEY_API_KEY\nx-portkey-config: YOUR_CONFIG_ID\nx-portkey-trace-id: my-project",
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "claude-sonnet-4-20250514",
    "ANTHROPIC_DEFAULT_OPUS_MODEL": "claude-opus-4-20250514",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL": "claude-haiku-4-20250514"
  },
  "model": "claude-sonnet-4-20250514"
}

With Portkey Config:

{
  "strategy": { "mode": "fallback" },
  "targets": [
    { "provider": "@vertex-prod" },
    { "provider": "@anthropic-prod" }
  ],
  "cache": { "mode": "simple" },
  "retry": { "attempts": 3, "on_status_codes": [429, 500, 502, 503] },
  "forward_headers": ["anthropic-beta"]
}

3. Set Up Enterprise Governance

Why Enterprise Governance?

Cost Management: Controlling and tracking AI spending across teams
Access Control: Managing team access and workspaces
Usage Analytics: Understanding how AI is being used across the organization
Security & Compliance: Maintaining enterprise security standards
Reliability: Ensuring consistent service across all users
Model Management: Managing what models are being used in your setup

Portkey adds a comprehensive governance layer to address these enterprise needs. Enterprise Implementation Guide

Step 1: Implement Budget Controls & Rate Limits

Model Catalog enables you to have granular control over LLM access at the team/department level. This helps you:

Set up budget limits
Prevent unexpected usage spikes using Rate limits
Track departmental spending

Setting Up Department-Specific Controls:

Navigate to Model Catalog in Portkey dashboard
Create new Provider for each engineering team with budget limits and rate limits
Configure department-specific limits

Step 2: Define Model Access Rules

As your AI usage scales, controlling which teams can access specific models becomes crucial. You can simply manage AI models in your org by provisioning model at the top integration level.

Step 4: Set Routing Configuration

Portkey allows you to control your routing logic very simply with it’s Configs feature. Portkey Configs provide this control layer with things like:

Data Protection: Implement guardrails for sensitive code and data
Reliability Controls: Add fallbacks, load-balance, retry and smart conditional routing logic
Caching: Implement Simple and Semantic Caching. and more…

Example Configuration:

Here’s a basic configuration to load-balance requests to OpenAI and Anthropic:

{
	"strategy": {
		"mode": "load-balance"
	},
	"targets": [
		{
			"override_params": {
				"model": "@YOUR_OPENAI_PROVIDER_SLUG/gpt-model"
			}
		},
		{
			"override_params": {
				"model": "@YOUR_ANTHROPIC_PROVIDER/claude-sonnet-model"
			}
		}
	]
}

Create your config on the Configs page in your Portkey dashboard. You’ll need the config ID for connecting.

Configs can be updated anytime to adjust controls without affecting running applications.

Step 4: Implement Access Controls

Step 3: Implement Access Controls

Create User-specific API keys that automatically:

Track usage per developer/team with the help of metadata
Apply appropriate configs to route requests
Collect relevant metadata to filter logs
Enforce access permissions

Create API keys through:

Example using Python SDK:

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")

api_key = portkey.api_keys.create(
    name="frontend-engineering",
    type="organisation",
    workspace_id="YOUR_WORKSPACE_ID",
    defaults={
        "config_id": "your-config-id",
        "metadata": {
            "environment": "development",
            "department": "engineering",
            "team": "frontend"
        }
    },
    scopes=["logs.view", "configs.read"]
)

For detailed key management instructions, see our API Keys documentation.

Step 5: Deploy & Monitor

Step 4: Deploy & Monitor

After distributing API keys to your engineering teams, your enterprise-ready setup is ready to go. Each developer can now use their designated API keys with appropriate access levels and budget controls. Apply your governance setup using the integration steps from earlier sections Monitor usage in Portkey dashboard:

Cost tracking by engineering team
Model usage patterns for AI agent tasks
Request volumes
Error rates and debugging logs

Enterprise Features Now Available

You now have:

Departmental budget controls
Model access governance
Usage tracking & attribution
Security guardrails
Reliability features

Portkey Features

Now that you have an enterprise-grade setup, let’s explore the comprehensive features Portkey provides to ensure secure, efficient, and cost-effective AI operations.

1. Comprehensive Metrics

Using Portkey you can track 40+ key metrics including cost, token usage, response time, and performance across all your LLM providers in real time. You can also filter these metrics based on custom metadata that you can set in your configs. Learn more about metadata here.

2. Advanced Logs

Portkey’s logging dashboard provides detailed logs for every request made to your LLMs. These logs include:

Complete request and response tracking
Metadata tags for filtering
Cost attribution and much more…

3. Unified Access to 1600+ LLMs

You can easily switch between 1600+ LLMs. Call various LLMs such as Anthropic, Gemini, Mistral, Azure OpenAI, Google Vertex AI, AWS Bedrock, and many more by simply changing the virtual key in your default config object.

4. Advanced Metadata Tracking

Using Portkey, you can add custom metadata to your LLM requests for detailed tracking and analytics. Use metadata tags to filter logs, track usage, and attribute costs across departments and teams.

Custom Metata

5. Enterprise Access Management

Budget Controls

Set and manage spending limits across teams and departments. Control costs with granular budget limits and usage tracking.

Single Sign-On (SSO)

Enterprise-grade SSO integration with support for SAML 2.0, Okta, Azure AD, and custom providers for secure authentication.

Organization Management

Hierarchical organization structure with workspaces, teams, and role-based access control for enterprise-scale deployments.

Access Rules & Audit Logs

Comprehensive access control rules and detailed audit logging for security compliance and usage tracking.

6. Reliability Features

Fallbacks

Automatically switch to backup targets if the primary target fails.

Conditional Routing

Route requests to different targets based on specified conditions.

Load Balancing

Distribute requests across multiple targets based on defined weights.

Caching

Enable caching of responses to improve performance and reduce costs.

Smart Retries

Automatic retry handling with exponential backoff for failed requests

Budget Limits

Set and manage budget limits across teams and departments. Control costs with granular budget limits and usage tracking.

7. Advanced Guardrails

Protect your Project’s data and enhance reliability with real-time checks on LLM inputs and outputs. Leverage guardrails to:

Prevent sensitive data leaks
Enforce compliance with organizational policies
PII detection and masking
Content filtering
Custom security rules
Data compliance checks

Guardrails

Implement real-time protection for your LLM interactions with automatic detection and filtering of sensitive content, PII, and custom security rules. Enable comprehensive data protection while maintaining compliance with organizational policies.

FAQs

How do I update my Virtual Key limits after creation?

You can update your Virtual Key limits at any time from the Portkey dashboard:1. Go to Virtual Keys section2. Click on the Virtual Key you want to modify3. Update the budget or rate limits4. Save your changes

Can I use multiple LLM providers with the same API key?

Yes! You can create multiple Virtual Keys (one for each provider) and attach them to a single config. This config can then be connected to your API key, allowing you to use multiple providers through a single API key.

How do I track costs for different teams?

Portkey provides several ways to track team costs:

Create separate Virtual Keys for each team
Use metadata tags in your configs
Set up team-specific API keys
Monitor usage in the analytics dashboard

What happens if a team exceeds their budget limit?

When a team reaches their budget limit:

Further requests will be blocked
Team admins receive notifications
Usage statistics remain available in dashboard
Limits can be adjusted if needed

Next Steps

Join our Community

For enterprise support and custom features, contact our enterprise team.

Ecosystem

LLM Integrations

Cloud Platforms

Guardrails

Plugins

Vector Databases

Agents

AI Apps

Libraries

Tracing Providers

MCP Clients

MCP Servers

​Quick Start

​Forward Headers (Required for Some Features)

​Trace Requests

​Advanced Configuration

​Fallbacks

​Load Balancing

​Caching

​Retries

​Budget Limits

​Troubleshooting

​Error: API Error: 500 Message: fetch failed

​Lightning Symbol (⚡) in Logs

​Claude Code Version Issues

​Complete Example

​3. Set Up Enterprise Governance

​Step 1: Implement Budget Controls & Rate Limits

​Setting Up Department-Specific Controls:

​Step 2: Define Model Access Rules

​Example Configuration:

​Step 3: Implement Access Controls

​Step 4: Deploy & Monitor

​Enterprise Features Now Available

​Portkey Features

​1. Comprehensive Metrics

​2. Advanced Logs

​3. Unified Access to 1600+ LLMs

​4. Advanced Metadata Tracking

Custom Metata

​5. Enterprise Access Management

Budget Controls

Single Sign-On (SSO)

Organization Management

Access Rules & Audit Logs

​6. Reliability Features

Fallbacks

Conditional Routing

Load Balancing

Caching

Smart Retries

Budget Limits

​7. Advanced Guardrails

Guardrails

​FAQs

​Next Steps

Quick Start

Forward Headers (Required for Some Features)

Trace Requests

Advanced Configuration

Fallbacks

Load Balancing

Caching

Retries

Budget Limits

Troubleshooting

Error: `API Error: 500 Message: fetch failed`

Lightning Symbol (⚡) in Logs

Claude Code Version Issues

Complete Example

3. Set Up Enterprise Governance

Step 1: Implement Budget Controls & Rate Limits

Setting Up Department-Specific Controls:

Step 2: Define Model Access Rules

Example Configuration:

Step 3: Implement Access Controls

Step 4: Deploy & Monitor

Enterprise Features Now Available

Portkey Features

1. Comprehensive Metrics

2. Advanced Logs

3. Unified Access to 1600+ LLMs

4. Advanced Metadata Tracking

5. Enterprise Access Management

6. Reliability Features

7. Advanced Guardrails

FAQs

Next Steps