Scaling LLM Traffic: Load Balancing Patterns Explained

Building resilient gateways for LLM requests is essential for production applications.

The Architecture

A load balancer interceptor prevents individual endpoints from becoming overwhelmed. By rotating traffic, you ensure that no single service node incurs the entire brunt of concurrent user queries.

Implementation

class LoadBalancer:
    def __init__(self, endpoints):
        self.endpoints = endpoints
        self.index = 0
    def get_next(self):
        endpoint = self.endpoints[self.index]
        self.index = (self.index + 1) % len(self.endpoints)
        return endpoint

Best Practices

• Always maintain an updated list of active model endpoints.
• Consider token-limit constraints when mapping requests.
• Use a persistent pointer to rotate through nodes sequentially.

Key takeaway: Resilience in AI apps starts with distributing your traffic load.

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