The Model Catalog is a centralized hub for viewing and managing all AI providers and models within your organization. It abstracts raw API keys and scattered environment variables into governed Provider Integrations and Models, giving you complete control over how your teams access and use AI.

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Upgrading from Virtual Keys

The Model Catalog upgrades the Virtual Key experience by introducing a centralized, organization-level management layer, offering advantages like:
  • Centralized provider and model management - no more duplicate configs across workspaces.
  • Fine-grained control: budgets, rate limits, and model allow-lists at both org and workspace level.
  • Inline usage: just pass model="@provider/model_slug"
Need help? See our Migration Guide ➜
Model Catalog - Provider and Models

AI Providers

AI Providers represent connections to AI services. Each AI Provider has:
  • ✅ A unique slug (e.g., @openai-prod)
  • ✅ Securely stored credentials
  • ✅ Budget and rate limits
  • ✅ Access to specific models

Models

The Models section is a gallery of all AI models available. Each Model entry includes:
  • ✅ Model slug (@openai-prod/gpt-4o)
  • ✅ Ready-to-use code snippets
  • ✅ Input/output token limits
  • ✅ Pricing information (where available)

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Adding an AI Provider

You can add providers via UI (follow the steps below) or API.
1

Go to AI Providers → Add Provider

Portkey Model Catalog - Add Provider
2

Select the AI Service to integrate

Choose from list (OpenAI, Anthropic, etc.) or Self-hosted / Custom.
Portkey Model Catalog - Add Provider - Choose Service
3

Enter Credentials

Choose existing credentials or create new ones.
Model Catalog - Add credentials
4

Enter provider details & save

Choose the name and slug for this provider. The slug cannot be changed later and will be used to reference the AI models.
Model Catalog - Add Provider Details

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Using Provider Models

Once you have AI Providers set up, you can use their models in your applications through various methods. In Portkey, model strings follow this format: @provider_slug/model_name
Model String Format
For example, @openai-prod/gpt-4o, @anthropic/claude-3-sonnet, @bedrock-us/claude-3-sonnet-v1 
import { Portkey } from 'portkey-ai';
const client = new Portkey({ apiKey: "PORTKEY_API_KEY" });

const resp = await client.chat.completions.create({
  model: '@openai-prod/gpt-4o',
  messages: [{ role: 'user', content: 'Hello!' }]
});

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2. Using the provider header

You can also specify the provider in the header instead of the model string. Remember to add the @ before your provider slug. 
import { Portkey } from 'portkey-ai';
const client = new Portkey({
	apiKey: "PORTKEY_API_KEY",
	provider: "@openai-prod"
});

const resp = await client.chat.completions.create({
  model: 'gpt-4o',
  messages: [{ role: 'user', content: 'Hello!' }]
});

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3. Specify provider in the config

Portkey’s configs are simple JSON structures that help you define routing logic for LLM requests. You can learn more about them here. Portkey’s config allows you to declare either the provider OR provider+model configuration in your routing config. Here’s how: 1. Defining the Provider 
// Specify provider in the config
{
	"provider": "@openai-prod"
}
2. Defining the Provider + Model 
// Specify the model string in "override_params"
{
  "override_params": {
    "model": "@openai-prod/gpt-4o"
  }
}
Using overide_params in strategy 
{
	"strategy": { "mode": "fallback" },
	"targets": [{
		"override_params": { "model": "@openai-prod/gpt-4o" }
	}, {
		"override_params": { "model": "@anthropic/claude-3-sonnet" }
	}]
}
Ordering: config (if provided) defines base; override_params merges on top (last write wins for scalars, deep merge for objects like metadata).

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Integrations

At the heart of Model Catalog is a simple concept: your AI provider credentials need to be stored securely, governed carefully and managed centrally. In Portkey, these stored credentials are called Integrations. Think of an Integration as a secure vault for your API keys - whether it’s your OpenAI API key, AWS Bedrock credentials, or Azure OpenAI configuration.
Integrations Overview Page
Once you create an Integration (by storing your credentials), you can use it to create multiple AI Providers. For example, you might have one OpenAI Integration, but create three different AI Providers from it:
  • @openai-dev for development with strict rate limits
  • @openai-staging for testing with moderate budgets
  • @openai-prod for production with higher limits
This separation gives you granular control over how different teams and environments use the same underlying credentials.

Integrations

Learn how to create and manage AI service credentials across your organization

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Managing Access and Controls

Each Integration in Portkey acts as a control point where you can configure:

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Budget Limits

Set spending controls at the Integration level to prevent unexpected costs. You can configure:
  • Cost-based limits: Maximum spend in USD (e.g., $1000/month)
  • Token-based limits: Maximum tokens consumed (e.g., 10M tokens/week)
  • Periodic resets: Weekly or monthly budget refreshes
Budget Limits Configuration
These limits cascade down to all AI Providers created from that Integration.

Budget Management

Set up cost controls and spending limits for your AI usage

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Rate Limits

Control request rates to manage load and prevent abuse:
  • Requests per minute/hour/day: Set appropriate throughput limits
  • Concurrent request limits: Control parallel processing
  • Burst protection: Prevent sudden spikes in usage
Rate limits help you maintain service quality and prevent any single user or team from monopolizing resources.

Rate Limiting

Configure request rate controls to ensure fair usage and prevent abuse

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Workspace Provisioning

Control which workspaces in your organization can access specific AI Providers:
  • Selective access: Choose which teams can use production vs development providers
  • Environment isolation: Keep staging and production resources separate
  • Department-level control: Give finance different access than engineering
Workspace Provisioning Interface
This hierarchical approach ensures teams only have access to the resources they need.

Workspace Provisioning

Manage workspace access to AI providers and models

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Model Provisioning

Fine-tune which models are available through each Integration:
  • Model allowlists: Only expose specific models (e.g., only GPT-4 for production)
  • Model denylists: Block access to expensive or experimental models
  • Custom model addition: Add your fine-tuned or self-hosted models
Model Provisioning Settings
Model provisioning helps you maintain consistency and control costs across your organization.

Model Provisioning

Configure which models are available through each integration

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Advanced Model Management

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Custom Models

The Model Catalog isn’t limited to standard provider models. You can add:
  • Fine-tuned models: Your custom OpenAI or Anthropic fine-tunes
  • Self-hosted models: Models running on your infrastructure
  • Private models: Internal models not publicly available
Each custom model gets the same governance controls as standard models.

Custom Models

Add and manage your fine-tuned, self-hosted, or private models

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Overriding Model Details (Custom Pricing)

Override default model pricing for:
  • Negotiated rates: If you have enterprise agreements with providers
  • Internal chargebacks: Set custom rates for internal cost allocation
  • Free tier models: Mark certain models as free for specific teams
Custom pricing ensures your cost tracking accurately reflects your actual spend.

Custom Pricing

Configure custom pricing for models with special rates

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Self-hosted AI Providers

Coming Soon