Learn how to integrate Portkey’s enterprise features with Cursor for enhanced observability, reliability and governance.
Cursor is a powerful AI-first code editor designed to streamline software development with built-in chat, autocomplete, and AI-powered refactoring tools. By integrating Portkey as the Gateway for your OpenAI API key, you can secure, monitor, and optimize all your LLM traffic—while gaining centralized visibility, caching, cost control, and enterprise-grade governance.
However, Portkey enables robust chat functionality, prompt management, observability, and token-level insights—perfect for teams that want more control over their API usage and compliance while still using Cursor’s interface.
Why Integrate Portkey with Cursor?
If you are an enterprise looking to use Cursor in your organisation, check out this section.
When you use Portkey with Cursor, you won’t have access to some Cursor-specific features that rely on their proprietary models—such as AI autocomplete, “Apply from Chat”, or inline refactoring. These are only available on Cursor’s Pro and Enterprise plans.
Portkey allows you to use 1600+ LLMs with your Cursor setup, with minimal configuration required. Let’s set up the core components in Portkey that you’ll need for integration.
Create an Integration
Navigate to the Integrations section on Portkey’s Sidebar. This is where you’ll connect your LLM providers.
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.
Configure Models
On the model provisioning page:
Click Create Integration to complete the integration
Copy the Provider Slug
Once your Integration is created:
openai-dev
)This slug is your provider’s unique identifier - you’ll need it for the next step.
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).
Configure Portkey API Key
Finally, create a Portkey API key:
Save your API key securely - you’ll need it for Cursor integration.
🎉 Voila, Setup complete! You now have everything needed to integrate Portkey with your application.
You will need your Portkey API key created in Step 1 for this integration
Portkey is an OpenAI compatible API, which means it can be easily integrated with Cursor without any changes to your setup. Here’s how you do it
To access Cursor’s settings and configure it for OpenAI integration, here are the key steps:
Open Settings: Click on “Cursor” in the menu bar and select “Settings…” and choose Cursor Settings.
In the Cursor Settings window, navigate to the Models tab.
Scroll down to find the API Keys section.
Add Your API Keys: Enable the the OpenAI API Key Toggle add you your Portkey API Key.
Toggle on the Override OpenAI Base URL and Enter Portkey’s Base URL: https://api.portkey.ai/v1
Click on Verify.
That’s it! now you have succesfully integrated Cursor with Portkey.
Why Enterprise Governance? If you are using Cursor inside your orgnaization, you need to consider several governance aspects:
Portkey adds a comprehensive governance layer to address these enterprise
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:
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:
Here’s a basic configuration to load-balance requests to OpenAI and Anthropic:
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
Create User-specific API keys that automatically:
Create API keys through:
Example using Python SDK:
For detailed key management instructions, see our API Keys documentation.
Step 5: 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:
Cursor now has:
Now that you have enterprise-grade Cursor setup, let’s explore the comprehensive features Portkey provides to ensure secure, efficient, and cost-effective AI operations.
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.
Portkey’s logging dashboard provides detailed logs for every request made to your LLMs. These logs include:
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.
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.
Set and manage spending limits across teams and departments. Control costs with granular budget limits and usage tracking.
Enterprise-grade SSO integration with support for SAML 2.0, Okta, Azure AD, and custom providers for secure authentication.
Hierarchical organization structure with workspaces, teams, and role-based access control for enterprise-scale deployments.
Comprehensive access control rules and detailed audit logging for security compliance and usage tracking.
Automatically switch to backup targets if the primary target fails.
Route requests to different targets based on specified conditions.
Distribute requests across multiple targets based on defined weights.
Enable caching of responses to improve performance and reduce costs.
Automatic retry handling with exponential backoff for failed requests
Set and manage budget limits across teams and departments. Control costs with granular budget limits and usage tracking.
Protect your Project’s data and enhance reliability with real-time checks on LLM inputs and outputs. Leverage guardrails to:
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.
How do I update my Provider Budget and Rate limits after creation?
You can update your AI Providers limits at any time from the Portkey dashboard.
Can I use multiple LLM providers with the same API key?
Yes! You can create multiple Integrations (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:
What happens if a team exceeds their budget limit?
When a team reaches their budget limit:
Join our Community
For enterprise support and custom features, contact our enterprise team.
Learn how to integrate Portkey’s enterprise features with Cursor for enhanced observability, reliability and governance.
Cursor is a powerful AI-first code editor designed to streamline software development with built-in chat, autocomplete, and AI-powered refactoring tools. By integrating Portkey as the Gateway for your OpenAI API key, you can secure, monitor, and optimize all your LLM traffic—while gaining centralized visibility, caching, cost control, and enterprise-grade governance.
However, Portkey enables robust chat functionality, prompt management, observability, and token-level insights—perfect for teams that want more control over their API usage and compliance while still using Cursor’s interface.
Why Integrate Portkey with Cursor?
If you are an enterprise looking to use Cursor in your organisation, check out this section.
When you use Portkey with Cursor, you won’t have access to some Cursor-specific features that rely on their proprietary models—such as AI autocomplete, “Apply from Chat”, or inline refactoring. These are only available on Cursor’s Pro and Enterprise plans.
Portkey allows you to use 1600+ LLMs with your Cursor setup, with minimal configuration required. Let’s set up the core components in Portkey that you’ll need for integration.
Create an Integration
Navigate to the Integrations section on Portkey’s Sidebar. This is where you’ll connect your LLM providers.
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.
Configure Models
On the model provisioning page:
Click Create Integration to complete the integration
Copy the Provider Slug
Once your Integration is created:
openai-dev
)This slug is your provider’s unique identifier - you’ll need it for the next step.
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).
Configure Portkey API Key
Finally, create a Portkey API key:
Save your API key securely - you’ll need it for Cursor integration.
🎉 Voila, Setup complete! You now have everything needed to integrate Portkey with your application.
You will need your Portkey API key created in Step 1 for this integration
Portkey is an OpenAI compatible API, which means it can be easily integrated with Cursor without any changes to your setup. Here’s how you do it
To access Cursor’s settings and configure it for OpenAI integration, here are the key steps:
Open Settings: Click on “Cursor” in the menu bar and select “Settings…” and choose Cursor Settings.
In the Cursor Settings window, navigate to the Models tab.
Scroll down to find the API Keys section.
Add Your API Keys: Enable the the OpenAI API Key Toggle add you your Portkey API Key.
Toggle on the Override OpenAI Base URL and Enter Portkey’s Base URL: https://api.portkey.ai/v1
Click on Verify.
That’s it! now you have succesfully integrated Cursor with Portkey.
Why Enterprise Governance? If you are using Cursor inside your orgnaization, you need to consider several governance aspects:
Portkey adds a comprehensive governance layer to address these enterprise
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:
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:
Here’s a basic configuration to load-balance requests to OpenAI and Anthropic:
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
Create User-specific API keys that automatically:
Create API keys through:
Example using Python SDK:
For detailed key management instructions, see our API Keys documentation.
Step 5: 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:
Cursor now has:
Now that you have enterprise-grade Cursor setup, let’s explore the comprehensive features Portkey provides to ensure secure, efficient, and cost-effective AI operations.
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.
Portkey’s logging dashboard provides detailed logs for every request made to your LLMs. These logs include:
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.
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.
Set and manage spending limits across teams and departments. Control costs with granular budget limits and usage tracking.
Enterprise-grade SSO integration with support for SAML 2.0, Okta, Azure AD, and custom providers for secure authentication.
Hierarchical organization structure with workspaces, teams, and role-based access control for enterprise-scale deployments.
Comprehensive access control rules and detailed audit logging for security compliance and usage tracking.
Automatically switch to backup targets if the primary target fails.
Route requests to different targets based on specified conditions.
Distribute requests across multiple targets based on defined weights.
Enable caching of responses to improve performance and reduce costs.
Automatic retry handling with exponential backoff for failed requests
Set and manage budget limits across teams and departments. Control costs with granular budget limits and usage tracking.
Protect your Project’s data and enhance reliability with real-time checks on LLM inputs and outputs. Leverage guardrails to:
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.
How do I update my Provider Budget and Rate limits after creation?
You can update your AI Providers limits at any time from the Portkey dashboard.
Can I use multiple LLM providers with the same API key?
Yes! You can create multiple Integrations (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:
What happens if a team exceeds their budget limit?
When a team reaches their budget limit:
Join our Community
For enterprise support and custom features, contact our enterprise team.