Android Studio

Add observability, governance, and reliability to Android Studio with Portkey.

Android Studio lets you use remote AI models (e.g. ChatGPT, Claude) for chat and Agent features. Add Portkey as your model provider to get:

• 1600+ LLMs — Use any provider (OpenAI, Anthropic, Google, etc.) through one gateway
• Observability — Track costs, tokens, and latency for every request
• Governance — Budget limits, usage tracking, and team access controls
• Reliability — Automatic fallbacks, retries, and caching

This guide shows how to add Portkey as a Third-Party Remote Provider in Android Studio in a few minutes.

For enterprise deployments across teams, see Enterprise Governance.

1. Setup

1. Add Provider

   Go to Model Catalog → Add Provider.

   
2. Configure Credentials

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

   
3. Get Portkey API Key

   Go to API Keys and create a Portkey API key. Optionally attach a Config for fallbacks, load balancing, or caching.

2. Add Portkey as a remote model provider

Follow Android Studio’s Use a remote model flow and use Portkey as the provider:

1. Open Model Providers

   In Android Studio:

   1. Open Settings (macOS: Cmd + , / Windows & Linux: Ctrl + Alt + S)
   2. Expand Tools → AI and select Model Providers
2. Add Third-Party Remote Provider

   1. Click the Add button
   2. Select Third-Party Remote Provider
   3. Enter the provider details:
      • Description: Portkey (or any name you prefer)
      • URL: https://api.portkey.ai/v1
      • API key: Your Portkey API key from step 1
   4. Click Refresh to retrieve the list of available models from Portkey
   5. Select the models you want to use (you can select multiple and switch between them in chat)
   6. Click OK to save

   

Done! Chat and Agent requests in Android Studio will go through Portkey. Monitor usage in the Portkey Dashboard.

Select a model for AI assistance

After adding Portkey as a provider:

1. Open the AI chat window in Android Studio
2. Use the model picker to choose a remote model from the list you configured

The models shown are the ones you added in Portkey (e.g. @openai-prod/gpt-4o, @anthropic-prod/claude-3-5-sonnet-20241022). All traffic is routed through Portkey automatically.

Fallbacks, load balancing, or caching? Create a Portkey Config, attach it to your API key, and use the config’s virtual model in Android Studio. See Enterprise Governance for examples.

3. Set Up Enterprise Governance

Why 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

Portkey adds a comprehensive governance layer to address these enterprise needs.

Enterprise 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
• Prevent unexpected usage spikes using Rate limits
• Track departmental spending

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

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…

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

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

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

Enterprise Features Now Available

You now have:

• Departmental budget controls
• Model access governance
• Usage tracking & attribution
• Security guardrails
• Reliability features

Portkey Features

Now that you have an enterprise-grade setup, let’s explore the comprehensive features Portkey provides to ensure secure, efficient, and cost-effective AI operations.

1. Comprehensive Metrics

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.

2. Advanced Logs

Portkey’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 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.

Custom Metata

5. Enterprise 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)

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

Organization Management

Hierarchical organization structure with workspaces, teams, and role-based access control for enterprise-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 organizational 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 organizational 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?

Portkey 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

• Discord Community
• GitHub Repository

For enterprise support and custom features, contact our enterprise team.