> ## Documentation Index
> Fetch the complete documentation index at: https://docs.portkey.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Continue.dev

> Add observability, cost tracking, and governance to your Continue.dev AI coding assistant

Continue.dev is an open-source AI coding assistant for VS Code and JetBrains. Add Prisma AIRS AI Gateway to get:

* **3,000+ LLMs** through one interface - switch providers instantly
* **Observability** - track costs, tokens, and latency for every request
* **Reliability** - automatic fallbacks, retries, and caching
* **Governance** - budget limits, usage tracking, and team access controls

This guide shows how to configure Continue.dev with the AI Gateway in under 5 minutes.

> [!NOTE]
>
> For deployments across teams, see [Set Up Governance](#3-set-up-governance).

***

## Video Walkthrough

[Continue.dev + AI Gateway Integration](https://drive.google.com/file/d/1hbmdqMJvzchDkS207JPYPdUvUaHwt-9a/preview)

Setting up Continue.dev with the AI Gateway - full walkthrough

***

# 1. Setup

**1. Add Provider**

Go to [Model Catalog](https://stratacloudmanager.paloaltonetworks.com/) → **Add Provider**.

**2. Configure Credentials**

Select your provider (OpenAI, Anthropic, Vertex AI, Mistral, etc.), enter your API key, and create a slug like `openai-prod`.

![](https://mintcdn.com/portkey-docs/QKXLB-54q6gEhIad/images/product/model-catalog/create-provider-page.png?fit=max\&auto=format\&n=QKXLB-54q6gEhIad\&q=85\&s=8c2057f3a99c26fc84815b0ac329a7ca)

**3. Get AI Gateway API Key**

Go to [API Keys](https://stratacloudmanager.paloaltonetworks.com/) and generate your AI Gateway API key.

***

# 2. Configure Continue.dev

**1. Open config.yaml**

In VS Code, open the Continue sidebar with `Cmd/Ctrl + L`. Click the agent/model dropdown at the top of the chat input, hover over **Local Config**, and click the **gear icon** ⚙️.

This opens your local config file at:

* `~/.continue/config.yaml` (macOS / Linux)
* `%USERPROFILE%\.continue\config.yaml` (Windows)

**2. Add AI Gateway Configuration**

Paste the configuration for your provider. Continue treats the AI Gateway as an OpenAI-compatible endpoint.

**OpenAI (GPT-4o-mini)**

```yaml theme={"system"}
name: My Config
version: 0.0.1
schema: v1

models:
  - name: "Portkey - GPT-4o-mini"
    provider: openai
    model: gpt-4o-mini
    apiBase: "https://aigw.portkey.ai/v1"
    apiKey: "YOUR_PORTKEY_API_KEY"
    requestOptions:
      headers:
        x-portkey-provider: "@openai-prod"
    roles:
      - chat
      - edit
      - apply
```

**Anthropic (Claude Opus 4.6)**

```yaml theme={"system"}
name: My Config
version: 0.0.1
schema: v1

models:
  - name: "Portkey - Claude Opus 4.6"
    provider: openai
    model: claude-opus-4-6
    apiBase: "https://aigw.portkey.ai/v1"
    apiKey: "YOUR_PORTKEY_API_KEY"
    requestOptions:
      headers:
        x-portkey-provider: "@anthropic-prod"
    roles:
      - chat
      - edit
      - apply
```

**Google Vertex AI**

```yaml theme={"system"}
name: My Config
version: 0.0.1
schema: v1

models:
  - name: "Portkey - Gemini 2.5 Pro (Vertex)"
    provider: openai
    model: gemini-2.5-pro
    apiBase: "https://aigw.portkey.ai/v1"
    apiKey: "YOUR_PORTKEY_API_KEY"
    requestOptions:
      headers:
        x-portkey-provider: "@vertex-prod"
    roles:
      - chat
      - edit
      - apply
```

> [!NOTE]
>
> For Claude models on Vertex AI, use the Vertex model ID format: `anthropic.claude-opus-4@20250514` and point `x-portkey-provider` to your Vertex AI provider slug.

**Mistral**

```yaml theme={"system"}
name: My Config
version: 0.0.1
schema: v1

models:
  - name: "Portkey - Mistral Large"
    provider: openai
    model: mistral-large-latest
    apiBase: "https://aigw.portkey.ai/v1"
    apiKey: "YOUR_PORTKEY_API_KEY"
    requestOptions:
      headers:
        x-portkey-provider: "@mistral-prod"
    roles:
      - chat
      - edit
      - apply
```

Replace:

* `YOUR_PORTKEY_API_KEY` → your key from Step 1
* `@openai-prod` / `@anthropic-prod` / `@vertex-prod` / `@mistral-prod` → your actual provider slug from Model Catalog

**3. Select the Model in Continue**

Save `config.yaml`. Continue auto-reloads on save. In the sidebar dropdown, switch from **Default Assistant** to **Local Config**, then pick your AI Gateway model from the list.

> [!NOTE]
>
> If the model does not appear, ensure you selected **Local Config** in the agent selector — not the default assistant.

Done! Monitor usage in the [Strata Cloud Manager](https://stratacloudmanager.paloaltonetworks.com/).

***

## Switch Providers

Change models by updating the **model** field and `x-portkey-provider` header in `config.yaml`:

```
@openai-prod/gpt-4o-mini
@anthropic-prod/claude-opus-4-6
@vertex-prod/gemini-2.5-pro
@mistral-prod/mistral-large-latest
```

All requests route through the AI Gateway automatically.

## Add Multiple Models

You can define multiple models in `config.yaml` and switch between them from the Continue sidebar at any time:

```yaml theme={"system"}
models:
  - name: "Portkey - GPT-4o-mini"
    provider: openai
    model: gpt-4o-mini
    apiBase: "https://aigw.portkey.ai/v1"
    apiKey: "YOUR_PORTKEY_API_KEY"
    requestOptions:
      headers:
        x-portkey-provider: "@openai-prod"
    roles:
      - chat
      - edit
      - apply

  - name: "Portkey - Claude Opus 4.6"
    provider: openai
    model: claude-opus-4-6
    apiBase: "https://aigw.portkey.ai/v1"
    apiKey: "YOUR_PORTKEY_API_KEY"
    requestOptions:
      headers:
        x-portkey-provider: "@anthropic-prod"
    roles:
      - chat
      - edit
      - apply

  - name: "Portkey - Gemini 2.5 Pro"
    provider: openai
    model: gemini-2.5-pro
    apiBase: "https://aigw.portkey.ai/v1"
    apiKey: "YOUR_PORTKEY_API_KEY"
    requestOptions:
      headers:
        x-portkey-provider: "@vertex-prod"
    roles:
      - chat
      - edit
      - apply

  - name: "Portkey - Mistral Large"
    provider: openai
    model: mistral-large-latest
    apiBase: "https://aigw.portkey.ai/v1"
    apiKey: "YOUR_PORTKEY_API_KEY"
    requestOptions:
      headers:
        x-portkey-provider: "@mistral-prod"
    roles:
      - chat
      - edit
      - apply
```

> [!NOTE]
>
> **Want fallbacks, load balancing, or caching?** Create a [AI Gateway Config](https://docs.portkey.ai/docs/aigw/product/ai-gateway/configs.md), attach it to your API key, and set `x-portkey-config` to your Config ID in `requestOptions.headers`. See [Set Up Governance](#3-set-up-governance) for examples.

***

# 3. Set Up Governance

**Why 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 organisation
* **Security & Compliance**: Maintaining your organisation's security standards
* **Reliability**: Ensuring consistent service across all users
* **Model Management**: Managing what models are being used in your setup

The AI Gateway adds a governance layer to address these needs.

**Implementation Guide**

> **Step 1: Implement Budget Controls & Rate Limits**
>
> ### 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](https://docs.portkey.ai/docs/aigw/product/policies/budget-limits.md)
> * Prevent unexpected usage spikes using Rate limits
> * Track departmental spending
>
> #### Setting Up Department-Specific Controls:
>
> 1. Navigate to [Model Catalog](https://stratacloudmanager.paloaltonetworks.com/) in Strata Cloud Manager
> 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**
>
> ### Step 2: Define Model Access Rules
>
> As your AI usage scales, controlling which teams can access specific models becomes crucial. You can manage AI models in your organisation by provisioning models at the top integration level.

> **Step 3: Set Routing Configuration**
>
> ### Step 3: Set Routing Configuration
>
> The AI Gateway lets you control your routing logic with its Configs feature. AI Gateway 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
>
> #### Example Configuration:
>
> Here's a basic configuration to load-balance requests to OpenAI and Anthropic:
>
> ```json theme={"system"}
> {
> 	"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](https://stratacloudmanager.paloaltonetworks.com/) page in Strata Cloud Manager. You'll need the config ID for connecting.
>
> > [!NOTE]
> >
> > Configs can be updated anytime to adjust controls without affecting running applications.

> **Step 4: Implement Access Controls**
>
> ### Step 4: 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:
>
> * [Strata Cloud Manager](https://stratacloudmanager.paloaltonetworks.com/)
> * [API Key Management API](https://docs.portkey.ai/docs/api-reference/admin-api/control-plane/api-keys/create-api-key.md)
>
> Example using Python SDK:
>
> ```python theme={"system"}
> 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](https://docs.portkey.ai/docs/api-reference/admin-api/control-plane/api-keys/create-api-key.md).

> **Step 5: Deploy & Monitor**
>
> ### Step 5: Deploy & Monitor
>
> After distributing API keys to your engineering teams, your 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 the AI Gateway dashboard:
>
> * Cost tracking by engineering team
> * Model usage patterns for AI agent tasks
> * Request volumes
> * Error rates and debugging logs

> [!TIP]
>
> ### What's Now Available
>
> **You now have:**
>
> * Departmental budget controls
> * Model access governance
> * Usage tracking & attribution
> * Security guardrails
> * Reliability features

# AI Gateway Features

Now that your setup is in place, let's explore the features the AI Gateway provides to ensure secure, efficient, and cost-effective AI operations.

### 1. Comprehensive Metrics

Using the AI Gateway 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.

### 2. Advanced Logs

The AI Gateway'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...

### 3. Unified Access to 1600+ LLMs

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 `provider` slug in your default `config` object.

### 4. Advanced Metadata Tracking

Using the AI Gateway, 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.

> **[Custom Metadata](https://docs.portkey.ai/docs/aigw/product/observability/metadata.md)**

### 5. Access Management

> **[Budget Controls](https://docs.portkey.ai/docs/aigw/product/policies/budget-limits.md)**
>
> Set and manage spending limits across teams and departments. Control costs with granular budget limits and usage tracking.

> **[Single Sign-On (SSO)](https://docs.portkey.ai/docs/aigw/product/enterprise-offering/org-management/sso.md)**
>
> SSO integration with support for SAML 2.0, Okta, Azure AD, and custom providers for secure authentication.

> **[Organisation Management](https://docs.portkey.ai/docs/aigw/product/enterprise-offering/org-management.md)**
>
> Hierarchical organisation structure with workspaces, teams, and role-based access control for large-scale deployments.

> **[Access Rules & Audit Logs](https://docs.portkey.ai/docs/aigw/product/enterprise-offering/access-control-management.md#audit-logs)**
>
> Comprehensive access control rules and detailed audit logging for security compliance and usage tracking.

### 6. Reliability Features

> **[Fallbacks](https://docs.portkey.ai/docs/aigw/product/ai-gateway/fallbacks.md)**
>
> Automatically switch to backup targets if the primary target fails.

> **[Conditional Routing](https://docs.portkey.ai/docs/aigw/product/ai-gateway/conditional-routing.md)**
>
> Route requests to different targets based on specified conditions.

> **[Load Balancing](https://docs.portkey.ai/docs/aigw/product/ai-gateway/load-balancing.md)**
>
> Distribute requests across multiple targets based on defined weights.

> **[Caching](https://docs.portkey.ai/docs/aigw/product/ai-gateway/cache-simple-and-semantic.md)**
>
> Enable caching of responses to improve performance and reduce costs.

> **[Smart Retries](https://docs.portkey.ai/docs/aigw/product/ai-gateway/automatic-retries.md)**
>
> Automatic retry handling with exponential backoff for failed requests

> **[Budget Limits](https://docs.portkey.ai/docs/aigw/product/policies/budget-limits.md)**
>
> Set and manage budget limits across teams and departments. Control costs with granular budget limits and usage tracking.

### 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 organisational policies
* PII detection and masking
* Content filtering
* Custom security rules
* Data compliance checks

> **[Guardrails](https://docs.portkey.ai/docs/aigw/product/guardrails.md)**
>
> 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 organisational policies.

# FAQs

> **How do I update my AI Provider limits after creation?**
>
> Update AI Provider limits at any time from [Model Catalog](https://stratacloudmanager.paloaltonetworks.com/): 1. Open the provider you want to modify. 2. Update the budget or rate limits. 3. Save your changes.

> **Can I use multiple LLM providers with the same API key?**
>
> Yes! Add multiple AI Providers to Model Catalog (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?**
>
> The AI Gateway provides several ways to track team costs:
>
> * Create separate AI Providers for each team
> * Use metadata tags in your configs
> * Set up team-specific API keys
> * Monitor usage in the analytics dashboard

> **What happens if a team exceeds their budget limit?**
>
> When a team reaches their budget limit:
>
> 1. Further requests will be blocked
> 2. Team admins receive notifications
> 3. Usage statistics remain available in dashboard
> 4. Limits can be adjusted if needed

# Next Steps

**Join our Community**

* [GitHub Repository](https://github.com/Portkey-AI)


## Related topics

- [Claude Desktop for Developers](https://docs.portkey.ai/docs/aigw/integrations/libraries/claude-desktop-developers.md)
- [SSO](https://docs.portkey.ai/docs/aigw/product/enterprise-offering/org-management/sso.md)
- [Figma MCP server](https://docs.portkey.ai/docs/aigw/integrations/mcp-servers/figma-mcp-server.md)
- [Chat Completions](https://docs.portkey.ai/docs/aigw/product/ai-gateway/chat-completions.md)
- [Configs](https://docs.portkey.ai/docs/aigw/product/ai-gateway/configs.md)


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.