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 250+ LLMs with your Project setup, with minimal configuration required. Let’s set up the core components in Portkey that you’ll need for 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:

   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:

   {
     "virtual_key": "<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? When you are using any AI tool in an enterprise setting, you need to consider several governance aspects:

• Cost Management: Controlling and tracking AI spending across teams
• Access Control: Managing which teams can use specific models
• Usage Analytics: Understanding how AI is being used across the organization
• Security & Compliance: Maintaining enterprise security standards
• Reliability: Ensuring consistent service across all users

Portkey adds a comprehensive governance layer to address these enterprise needs. Let’s implement these controls step by step.

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Enterprise Implementation Guide

{
	"strategy": {
		"mode": "single"
	},
	"targets": [
		{
			"virtual_key": "YOUR_OPENAI_VIRTUAL_KEY",
			"override_params": {
				"model": "gpt-4o"
			}
		}
	]
}

from portkey_ai import Portkey

portkey = Portkey(api_key="YOUR_ADMIN_API_KEY")

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

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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

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