API Deployment

Introduction

Most AI features are exposed as APIs. Patterns: serverless functions, edge runtimes, long-running workers for slow generations, webhooks for async results.

Beginner analogy: Like deploying any REST API, but some requests legitimately take 30+ seconds.

Understanding the topic

Core concepts to understand:

• Serverless (Vercel, Cloudflare, AWS Lambda) — easy, but watch timeouts.
• Edge runtimes for low latency.
• Streaming responses via SSE / WebSockets.
• Async + webhooks for very long jobs (image gen, fine-tuning).

Syntax reference

Visual workflow / architecture:

Sync chat       → SSE  / serverless function
Long generation → queue → worker → webhook callback

Real-world use

Vercel's AI templates power thousands of production AI apps. Cloudflare Workers AI runs LLMs at the edge.

Best practices

• Always set generous timeouts (60s+).
• Use queues for slow jobs.
• Cache deterministic responses.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. How do you handle a 60-second LLM call in serverless?
• Q2. When use queues vs sync?
• Q3. Edge vs region for AI APIs?