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Productionizing LLM Gateways: Architecture, Tradeoffs, and Hard Lessons from the Trenches

Conference Context

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.

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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.

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