Productionizing LLM Gateways: Architecture, Tradeoffs and Hard Lessons — Kanish Manuja, Twilio
Kanish Manuja from Twilio explains how to build LLM gateways — middleware that routes requests between different AI models — and why it's harder than it seems.
A fundamental challenge is that you cannot maximize everything simultaneously: availability, speed, security, and cost often conflict. Once an AI model begins responding and streaming tokens, you cannot switch to another provider if something fails, so you must plan for backups per request instead of global retries. Different models are surprisingly different — a reasoning model taking 60 seconds is completely normal, whereas a chat model would be entirely down — so you must measure thresholds separately for each model and path. Many teams believe they need a central gateway, but often they only need centralized control over which models are used, which can be solved without centralizing all traffic.
Vibekollen prepared this summary with AI from the original publication. The content belongs to AI Engineer.
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