Scaling AI Systems

Introduction

Scaling AI features means handling traffic spikes, provider rate limits, multi-region deployments and graceful degradation when the LLM is slow.

Beginner analogy: Like scaling a normal SaaS, but your most expensive dependency has its own rate limits and outages.

Understanding the topic

Core concepts to understand:

• Spread load across multiple providers.
• Queue + worker for burst absorption.
• Provider rate-limit aware retries with jitter.
• Multi-region deployment for latency.
• Degrade gracefully — return cached / shorter answer on failure.

Syntax reference

Visual workflow / architecture:

Spike  ─► Queue ─► Workers ─► Provider A (primary)
                                  │
                                  ▼  rate-limited
                          Provider B (fallback)

Real-world use

Major launches (OpenAI o1, Claude 3.5) often saturate provider quotas. Big consumers like Perplexity negotiate dedicated capacity.

Best practices

• Always have fallback providers.
• Queue for burst.
• Negotiate dedicated capacity if you're large.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. How do you absorb traffic spikes for an AI feature?
• Q2. Multi-provider strategy?
• Q3. How do you degrade gracefully?