• Unified AI Gateway - Single interface for 1600+ LLMs with API key management. (not just OpenAI & Anthropic)
• CentraliCursor AI observability: Real-time usage tracking for 40+ key metrics and logs for every request
• Governance - Real-time spend tracking, set budget limits and RBAC in your Cursor setup
• Security Guardrails -a PII detection, content filtering, and compliance controls

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1. Setting up Portkey

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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   {
       "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 Cursor integration.

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2. Integrated Portkey with Cursor

1. Open Settings: Click on “Cursor” in the menu bar and select “Settings…” and choose Cursor Settings.
2. In the Cursor Settings window, navigate to the Models tab.
3. Scroll down to find the API Keys section.
4. Add Your API Keys: Enable the the OpenAI API Key Toggle add you your Portkey API Key.
5. Toggle on the Override OpenAI Base URL and Enter Portkey’s Base URL: https://api.portkey.ai/v1
6. Click on Verify.

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3. Set Up Enterprise Governance for Cursor

• 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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{
	"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 to Cursor’s setup.

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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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 Cursor 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