Surviving LLM Traffic Spikes: Routing, Rate Limits, and Failover in Python

Your team ships an AI feature and users love it. Then one viral post turns "normal load" into hundreds of LLM requests per second.

LLM calls don't behave like traditional API requests. They're slow (sometimes seconds), expensive, rate-limited by providers, and a single provider outage can take your entire feature down. You can't just "add more servers." You need a routing layer that knows where to send traffic, when to back off, and how to fail without taking everything with it.

In this talk, we'll walk through the LLM traffic routing architecture we built in Python at Manychat, where we serve AI-powered automation to thousands of Instagram and messaging accounts. Everything we'll show is running in production.

We'll cover the core gateway patterns for multi-provider LLM traffic, implemented using LiteLLM Router as a reference design.

By the end, you'll walk away with:

• A weighted routing blueprint you can adapt to your own provider mix
• Fallback and cooldown rules designed to survive real outages
• Practical rate limiting (requests and tokens) with retry backoff
• The monitoring baseline (latency, tokens, errors by provider, weight drift) to catch issues before they cascade
• A checklist for rolling this out safely, incrementally