AI Deployment Basics

Introduction

Deploying an AI feature ≠ deploying a regular API. You need to think about model versioning, prompt versioning, fallback providers, streaming infrastructure, cost monitoring and abuse protection.

Beginner analogy: Like deploying any web app, but the API you depend on is a $200B industry that updates weekly.

Understanding the topic

Core concepts to understand:

• Backend proxies the LLM API (never expose keys to frontend).
• Use SSE / WebSockets for streaming.
• Version both prompts and models.
• Add fallback model + retry on failure.
• Per-user rate limits + budget caps.

Syntax reference

Visual workflow / architecture:

Browser ──HTTPS──► Your backend ──HTTPS──► OpenAI / Anthropic
                          │
                          ├──► Logs & metrics
                          ├──► Rate limiter
                          └──► Fallback chain

Real-world use

Every production AI feature you've used follows this pattern. Vercel AI SDK + Next.js + serverless functions is a common stack.

Best practices

• Always proxy through your backend.
• Stream every response.
• Version everything.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Why proxy LLM calls through your backend?
• Q2. How do you handle provider outages?
• Q3. What do you version in an AI app?