---
title: "Productionizing LLM Gateways: Architecture, Tradeoffs, and Hard Lessons from the Trenches"
category: "talks"
date: "2026-06-29"
time: "2:25pm-2:45pm"
track: "AI-Native Enterprises"
room: "Leadership 1"
speakers: ["Kanish Manuja"]
sourceLabels: ["Official conference schedule", "Public YouTube metadata"]
scheduleTrack: "AI-Native Enterprises"
scheduleRoom: "Leadership 1"
scheduleLabels: ["AI-Native Enterprises", "Leadership 1", "session", "confirmed"]
---
# Productionizing LLM Gateways: Architecture, Tradeoffs, and Hard Lessons from the Trenches

## Conference Context
- Date/time: 2026-06-29 · 2:25pm-2:45pm
- Track/room: AI-Native Enterprises · Leadership 1
- Speaker(s): Kanish Manuja
- Session type/status: session · confirmed

- Track: AI-Native Enterprises
- Room: Leadership 1
- Session type: session
- Status: confirmed

## Session Description
As organizations scale their use of large language models, the biggest challenge is no longer prompting, it’s productionizing. This session dives deep into building and operating an LLM gateway that sits between applications and model providers, handling routing, observability, cost control, reliability, and safety at scale. Drawing from real world experience, this talk breaks down the architecture of a production LLM gateway, including model abstraction layers, request orchestration, fallback strategies, caching, rate limiting, and evaluation pipelines. We’ll explore hard tradeoffs such as latency vs. cost, quality vs. determinism, and vendor lock-in vs. flexibility. Attendees will leave with concrete design patterns, failure modes to avoid, and a mental model for turning LLM experiments into resilient, scalable systems.

## Media Evidence
No related AI Engineer channel video found yet.

## Evidence Graph
This evidence graph is generated from currently linked source material: official schedule text, related video pages, cached transcripts, visible slide text, dense/reconstructed slide pages, and AI slide-classification audits.

### Media Signals
No linked video, transcript, or slide source has been attached yet.

### Agent Reading Notes
Use these signals to refine the synopsis, topic links, people/company context, and method notes. If a source is a related external video rather than an exact official recording, keep it framed as supporting evidence.

## Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.

## People
- [[kanish-manuja]]

## Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.

## Synthesis
### Synthesized Breakdown
# Productionizing LLM Gateways: Architecture, Tradeoffs, and Hard Lessons from the Trenches ## Conference Context - Date/time: 2026-06-29 · 2:25pm-2:45pm - Track/room: AI-Native Enterprises · Leadership 1 - Speaker(s): Kanish Manuja - Session type/status: session · confirmed - Track: AI-Native Enterprises - Room: Leadership 1 - Session type: session - Status: confirmed ## Session Description As organizations scale their use of large language models, the biggest challenge is no longer prompting, it’s productionizing. This session dives deep into building and operating an LLM gateway that sits between applications and model providers, handling routing, observability, cost control, reliability, and safety at scale. Drawing from real world experience, this talk breaks down the architecture of a production LLM gateway, including model abstraction layers, request orchestration, fallback strategies, caching, rate limiting, and evaluation pipelines. We’ll explore hard tradeoffs such as latency vs.

### Speaker And Company Context
- [[kanish-manuja|Kanish Manuja]] — Principal Software Engineer at [[twilio-inc|Twilio Inc.]].

### Topics Covered
- [[agent-security]]

### Derived Links And Source Material

### Novel Concepts / Clever Methods
- No highlighted novel concept has been detected yet.

### Evidence Boundary
This synthesis is based on the official schedule and linked source pages. It should be revisited when exact session recordings or transcript-backed secondary sources are available.