Portkey enhances any OpenAI API compliant project by adding enterprise-grade features like observability, reliability, rate limiting, access control, and budget management—all without requiring code changes.

It is a drop-in replacement for your existing OpenAI-compatible applications. This guide explains how to integrate Portkey with minimal changes to your project settings.

While OpenAI (or any other provider) provides an API for AI model access. Commercial usage often require additional features like:

• Advanced Observability: Real-time usage tracking for 40+ key metrics and logs for every request
• Unified AI Gateway - Single interface for 250+ LLMs with API key management
• Governance - Real-time spend tracking, set budget limits and RBAC in your AI systems
• Security Guardrails - PII detection, content filtering, and compliance controls

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1. Getting Started with Portkey

Portkey allows you to use 1600+ LLMs with your n8n setup, with minimal configuration required. Let’s set up the core components in Portkey that you’ll need for integration.

1

Create an Integration

Navigate to the Integrations section on Portkey’s Sidebar. This is where you’ll connect your LLM providers.

1. Find your preferred provider (e.g., OpenAI, Anthropic, etc.)
2. Click Connect on the provider card
3. In the “Create New Integration” window:
   • Enter a Name for reference
   • Enter a Slug for the integration
   • Enter your API Key and other provider specific details for the provider
4. Click Next Step

In your next step you’ll see workspace provisioning options. You can select the default “Shared Team Workspace” if this is your first time OR chose your current one.

2

Configure Models

On the model provisioning page:

• Leave all models selected (or customize)
• Toggle Automatically enable new models if desired

Click Create Integration to complete the integration

3

Copy the Provider Slug

Once your Integration is created:

1. Go to Model Catalog → Models tab
2. Find and click on you your model button (if your model is not visible, you need to edit your integration from the last step)
3. Copy the slug (e.g., @openai-dev/gpt-4o)

We recommend clicking the Run Test Request button on this step to verify your integration. If you see the error: You do not have enough permissions to execute this request, you’ll need to create a User API Key for this step to work properly.

You can create one here. You should be able to see simple chat request output on this step.

This is your unique identifier - you’ll need it for the next step. This slug is basically @your-provider-slug/your-model-name

4

Create Default Config

Portkey’s config is a JSON object used to define routing rules for requests to your gateway. You can create these configs in the Portkey app and reference them in requests via the config ID. For this setup, we’ll create a simple config using your provider (OpenAI) and model (gpt-4o).

1. Go to Configs in Portkey dashboard
2. Create new config with:
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   Ask AI

   {
       "override_params": {
         "model": "@YOUR_SLUG-FROM-LAST_STEP" // example: @openai-test/gpt-4o-mini
       }
   }
   

3. Save and note the Config ID & Name for the next step

5

Configure Portkey API Key

Finally, create a Portkey API key:

1. Go to API Keys in Portkey
2. Create new API key
3. Select the config that you create from previous step
4. Generate and save your API key

Save your API key securely - you’ll need it for n8n integration.

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2. Integrating Portkey with Your Project

You can integrate Portkey with any OpenAI API-compatible project through a simple configuration change. This integration enables advanced monitoring, security features, and analytics for your LLM applications. Here’s how you do it:

1. Locate LLM Settings Navigate to your project’s LLM settings page and find the OpenAI configuration section (usually labeled ‘OpenAI-Compatible’ or ‘Generic OpenAI’).”

2. Configure Base URL Set the base URL to:

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   Ask AI

   https://api.portkey.ai/v1
   

3. Add API Key Enter your Portkey API key in the appropriate field. You can generate this key from your Portkey dashboard under API Keys section.

4. Configure Model Settings If your integration allows direct model configuration, you can specify it in the LLM settings. Otherwise, create a configuration object:

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   Ask AI

   {
     "provider":"@<YOUR_VIRTUAL_API_KEY>",
     "override_params": {
       "model": "gpt-4o" // Specify your desired model
     }
   }
   

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3. Set Up Enterprise Governance for Your Project

Why Enterprise Governance? If you are using Your Project inside your orgnaization, you need to consider several governance aspects:

• 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

Enterprise Implementation Guide

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Portkey Features

Now that you have set up your enterprise-grade Project environment, let’s explore the comprehensive features Portkey provides to ensure secure, efficient, and cost-effective AI operations.

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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.

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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…

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3. Unified Access to 250+ LLMs

You can easily switch between 250+ 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.

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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.

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5. Enterprise Access Management

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6. Reliability Features

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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

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FAQs

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Next Steps

Join our Community

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• GitHub Repository