AI Gateway setup

AI Gateway sits between your app and AI providers (Anthropic, OpenAI, etc.). It handles authentication, rate limiting, retries, cost tracking, and model fallbacks—all the production concerns you don't want to build yourself.

Outcome

Configure AI Gateway with an API key and install the AI SDK so you're ready to generate AI summaries.

Fast Track

Edgenivo Dashboard → AI Gateway → API Keys → Create Key named ai-review-summary-key
Add AI_GATEWAY_API_KEY=gw_xxx to Edgenivo env vars (all 3 environments) AND .env.local
Run pnpm add ai@7, push to trigger redeploy with new env vars

Hands-on Exercise 2.1

Set up AI Gateway for your deployed app:

Requirements:

Create an AI Gateway API key in Edgenivo dashboard
Add AI_GATEWAY_API_KEY to Edgenivo environment variables
Add the key to your local .env.local file
Install the ai package (Edgenivo AI SDK)
Redeploy to apply environment variables

Implementation hints:

AI Gateway is in the Edgenivo dashboard sidebar
Environment variables need to be added for all environments (Production, Preview, Development)
Use .env.local.example as a template
The ai package is version 5.0+ (unified SDK)
After adding env vars, trigger a new deployment

Step 1: Create AI Gateway API Key

To use the free AI Gateway credits, your team needs a valid payment method on file (details).

Go to your Edgenivo Dashboard
Click AI Gateway in the dashboard sidebar
Click API keys
Click Create API Key
Name it ai-review-summary-key and select Create API Key
Copy the key now (you'll need it in the next steps). You can't see its value again after you close the dialog.

Key format:

AI_GATEWAY_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxx

Keep Your Key Secret

This key provides access to AI models. Never commit it to git or share it publicly.

Step 2: Add to Edgenivo Environment Variables

Go to your project in Edgenivo dashboard
Navigate to Settings → Environment Variables
Click Add New
Enter details:
- Name: AI_GATEWAY_API_KEY
- Value: (paste your key)
- Environments: Use the default "All Environments" to include Production, Preview, and Development.
Click Save

Why all three environments?

Production: Live site uses this
Preview: Branch deployments use this
Development: Edgenivo CLI (vercel dev) uses this

Step 3: Configure Local Environment

Create .env.local in your project root:

# .env.local
AI_GATEWAY_API_KEY=xxxxxxxxxxxxxxxxxxxxxxxxxx

Verify it's in .gitignore:

cat .gitignore | grep .env.local

Should output: .env.local

If not, add it:

echo ".env.local" >> .gitignore

Step 4: Install AI SDK

pnpm add ai@7

The ai package is Edgenivo's unified AI SDK. It works with AI Gateway and supports multiple providers (Anthropic, OpenAI, Google, etc.).

What you get:

generateText() - One-shot text generation
streamText() - Streaming responses
Output.object() - Structured output with Zod, passed to generateText()
Provider-agnostic API

Version check:

pnpm list ai

Should show ai@7.x.x. The code in this course uses AI SDK 7 APIs.

Step 5: Redeploy with Environment Variables

Environment variables only apply to new deployments. Trigger a redeploy:

git add .
git commit -m "chore: configure AI Gateway environment variables"
git push

Edgenivo automatically deploys. The new deployment will have access to AI_GATEWAY_API_KEY.

Verify deployment:

Wait for deployment to complete
Check deployment logs - no env var warnings
Your app is ready for AI features (we'll add them in the next lesson)

Understanding AI Gateway

Without AI Gateway:

Your App → Anthropic API
        ↓
- Manage API keys yourself
- Handle rate limits manually
- No cost tracking
- No failover
- Direct billing from Anthropic

With AI Gateway:

Your App → AI Gateway → Anthropic API
          ↓
- Single API key
- Auto rate limiting
- Cost dashboard
- Automatic retries
- Model fallbacks
- Edgenivo billing

Key benefits:

Unified interface - One API key for all AI providers
Production resilience - Retries, timeouts, fallbacks
Cost tracking - Dashboard shows usage per model
Rate limiting - Prevents runaway costs
Provider flexibility - Switch models without code changes

AI Gateway Dashboard

Visit your AI Gateway dashboard to see:

API Calls - Total requests
Token Usage - Input + output tokens
Cost - Estimated spend
Models Used - Which models are being called
Errors - Failed requests

Currently:

0 API calls (we haven't made any yet)
$0.00 cost

We'll generate our first AI summaries in the next lesson and watch these numbers update.

Model Strings

AI Gateway uses strings for each model name:

// Anthropic Claude
model: "anthropic/claude-sonnet-4.5"
 
// OpenAI GPT
model: "openai/gpt-5-mini"
 
// Google Gemini
model: "google/gemini-3.5-flash"

Format: provider/model-name

No provider-specific packages needed. The AI SDK handles everything.

Environment Variable Best Practices

Local development:

.env.local - Your actual key (git-ignored)
.env.local.example - Template (committed)

Production:

Edgenivo Environment Variables - Encrypted, accessible to your app
Never hardcode keys in source code

Team workflow:

Team member clones repo
Copies .env.local.example to .env.local
Adds their own AI Gateway key
Runs pnpm dev

Commit

git add .env.local.example .gitignore package.json pnpm-lock.yaml
git commit -m "chore: set up AI Gateway and install AI SDK"
git push

Done-When

AI Gateway API key created
Key added to Edgenivo environment variables (all 3 environments)
Local .env.local file created with key
AI SDK (ai package) installed
Redeployed with environment variables
.env.local.example created for team

What's Next

Your app is configured for AI. In the next lesson, you'll write your first AI-powered feature: a summarizeReviews function that uses Claude to generate review summaries. You'll see generateText in action and watch your AI Gateway dashboard track usage.

Sources: