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Prerequisites

• Portkey API key
• Provider configured in Model Catalog
• Retool account with resource creation permissions

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Setup

1

Configure Portkey

1. Go to Model Catalog → Add Provider.
2. Select your provider (OpenAI, Anthropic, etc.), enter your API key, and create a slug like openai-prod.
3. Go to API Keys → Create new key → Save.

2

Create a Custom AI Provider resource in Retool

1. In Retool, go to Resources → Create new → Resource.
2. Search for Custom AI Provider and select it.
3. Give the resource a name (e.g., Portkey).

3

Configure the connection

Fill in the following fields:

Field	Value
Base URL	https://api.portkey.ai/v1
API Key	Your Portkey API key
Compatible Schema	OpenAI
Model Names	Use Portkey Model Catalog slugs (e.g., @openai-prod/gpt-5, @anthropic-prod/claude-opus-4-7)

Portkey model slugs follow the format @<provider-slug>/<model-id>, where the provider slug is the one you created in Model Catalog. This routes each request to the right provider and model automatically — no extra headers needed.

4

Save and verify

Click Create resource. To verify, create a query in your app or workflow, select the Portkey resource, choose Generate chat response, and run it.

Custom AI Provider resources don’t have a Test connection button. Verify by running a query.

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Use Portkey in queries

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Example: Chat query

System message:
You are a helpful assistant for our internal support team.

Prompt:
Analyze this customer feedback: {{ feedbackTextarea.value }}

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Advanced headers

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What you get with Portkey

• 1600+ LLMs — Switch between OpenAI, Anthropic, Google, Mistral, and more from the same Retool resource
• Observability — Every query logged with latency, cost, and token usage in the Portkey Dashboard
• Reliability — Automatic retries and fallbacks keep your Retool apps running
• Governance — Budget limits, rate limits, and RBAC per team or workspace
• Guardrails — PII detection and content filtering on requests and responses

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3. Set Up Enterprise 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 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:

{
	"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.

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:

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 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