LLM Gateway Architecture

Control LLM costs by enforcing token budgets at the gateway layer.

Context window limits vary wildly across providers in ways that silently break production systems.

A gateway layer between your app and Azure OpenAI absorbs rate limits before users see failures.

Queues prevent duplicate inference calls; pub/sub enables fan-out without coupling.

Coordinated retries can turn a provider blip into a cascading crisis across your infrastructure.

Distinguish network retries from sampling retries to avoid duplicate operations and silent failures.

Gateways must handle streaming responses as stateful flows, not complete payloads.

Token and cost constraints matter as much as request volume when designing LLM rate limits.

LLM requests demand four separate signal types that standard API monitoring misses entirely.

Managing credentials across multiple LLM providers requires abstraction, not just secrets storage.

An LLM gateway reads what's inside requests; a reverse proxy just forwards them blindly.

Simplify your infrastructure by centralizing LLM authentication, routing, and failover in one layer.

Multi-model production requires centralized control to prevent runaway spend and shadow AI.

Enforce LLM policy at every routing point, not just the edge gateway.