• Multiple Claude Providers - Route through Anthropic, Bedrock, or Vertex AI
• Observability - track costs, tokens, and latency for every request
• Reliability - automatic fallbacks, retries, and caching
• Governance - budget limits, usage tracking, and team access controls

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1. Setup

1

Add Provider

Go to Model Catalog → Add Provider.Select Anthropic, Bedrock, or Vertex AI as your provider.

2

Configure Credentials

Enter your API key and create a slug like anthropic-prod.

3

Get Portkey API Key

Go to API Keys and generate your Portkey API key.

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2. Configure Claude Code

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

​

Switch Providers

@anthropic-prod
@bedrock-prod
@vertex-prod

​

Max Plan / Anthropic OAuth (Enterprise)

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.portkey.ai",
    "ANTHROPIC_CUSTOM_HEADERS": "x-portkey-api-key: YOUR_PORTKEY_API_KEY"
  }
}

​

3. Set Up 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

Step 1: Implement Budget Controls & Rate Limits

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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

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Setting Up Department-Specific Controls:

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

Step 2: Define Model Access Rules

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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…

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Example Configuration:

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

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Ask AI

{
	"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

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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:

• Portkey App
• API Key Management API

Example using Python SDK:

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Ask AI

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

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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

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Portkey Features

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1. Comprehensive Metrics

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2. Advanced Logs

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

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3. Unified Access to 1600+ LLMs

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4. Advanced Metadata Tracking

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5. Enterprise Access Management

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6. Reliability Features

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7. Advanced Guardrails

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

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FAQs

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Next Steps

• Discord Community
• GitHub Repository