Goose is an open source AI agent that automates engineering tasks. 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 Goose with the AI Gateway in under 5 minutes.

For deployments across teams, see Set Up Governance.

1. Setup

Add Provider

Go to Model Catalog → Add Provider.

Configure Credentials

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

Get AI Gateway API Key

Go to API Keys and generate your AI Gateway API key.

2. Configure Goose

Launch Goose

Open Goose through the desktop app or run goose in your terminal.

Configure Provider

Navigate to LLM provider settings
Select OpenAI as your provider
Configure:
- Base URL: https://aigw.portkey.ai
- API Key: Your AI Gateway API key
- Model: @openai-prod/gpt-4o (or your provider slug + model)

Done! Monitor usage in the Strata Cloud Manager.

Switch Providers

Change models by updating the model field:

@anthropic-prod/claude-3-5-sonnet-20241022
@openai-prod/gpt-4o
@google-prod/gemini-2.0-flash-exp

All requests route through the AI Gateway automatically.

Want fallbacks, load balancing, or caching? Create a AI Gateway Config, attach it to your API key, and set Model to dummy. See 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

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

Setting Up Department-Specific Controls:

Navigate to Model Catalog in Strata Cloud Manager
Create new Provider for each engineering team with budget limits and rate limits
Configure department-specific limits

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

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:

{
	"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 Strata Cloud Manager. 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

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:

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

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

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

5. Access Management

Budget Controls

Set and manage spending limits across teams and departments. Control costs with granular budget limits and usage tracking.

Single Sign-On (SSO)

SSO integration with support for SAML 2.0, Okta, Azure AD, and custom providers for secure authentication.

Organisation Management

Hierarchical organisation structure with workspaces, teams, and role-based access control for large-scale deployments.

Access Rules & Audit Logs

Comprehensive access control rules and detailed audit logging for security compliance and usage tracking.

6. Reliability Features

Fallbacks

Automatically switch to backup targets if the primary target fails.

Conditional Routing

Route requests to different targets based on specified conditions.

Load Balancing

Distribute requests across multiple targets based on defined weights.

Caching

Enable caching of responses to improve performance and reduce costs.

Smart Retries

Automatic retry handling with exponential backoff for failed requests

Budget Limits

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

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: 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:

Further requests will be blocked
Team admins receive notifications
Usage statistics remain available in dashboard
Limits can be adjusted if needed

Next Steps

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GitHub Repository

​1. Setup

​2. Configure Goose

​Switch Providers

​3. Set Up Governance

​Step 1: Implement Budget Controls & Rate Limits

​Setting Up Department-Specific Controls:

​Step 2: Define Model Access Rules

​Step 3: Set Routing Configuration

​Example Configuration:

​Step 4: Implement Access Controls

​Step 5: Deploy & Monitor

​What’s Now Available

​AI Gateway Features

​1. Comprehensive Metrics

​2. Advanced Logs

​3. Unified Access to 1600+ LLMs

​4. Advanced Metadata Tracking

Custom Metadata

​5. Access Management

Budget Controls

Single Sign-On (SSO)

Organisation Management

Access Rules & Audit Logs

​6. Reliability Features

Fallbacks

Conditional Routing

Load Balancing

Caching

Smart Retries

Budget Limits

​7. Advanced Guardrails

Guardrails

​FAQs

​Next Steps

1. Setup

2. Configure Goose

Switch Providers

3. Set Up Governance

Step 1: Implement Budget Controls & Rate Limits

Setting Up Department-Specific Controls:

Step 2: Define Model Access Rules

Step 3: Set Routing Configuration

Example Configuration:

Step 4: Implement Access Controls

Step 5: Deploy & Monitor

What’s Now Available

AI Gateway Features

1. Comprehensive Metrics

2. Advanced Logs

3. Unified Access to 1600+ LLMs

4. Advanced Metadata Tracking

5. Access Management

6. Reliability Features

7. Advanced Guardrails

FAQs

Next Steps