Preferences > Benchmarks: Model Routing for How Teams Actually Build
Conference Context
- Date/time: 2026-07-01 · 12:05pm-12:25pm
- Track/room: AI Architects: AI Factories · Leadership 2
- Speaker(s): Archana Kamath, Tyler Gillam
- Session type/status: session · confirmed
- Track: AI Architects: AI Factories
- Room: Leadership 2
- Session type: session
- Status: confirmed
Session Description
There is no best model. There's only the right model for a given task, and the right model depends on your team's preferences, not a benchmark score. This talk makes the case for preference-aligned routing: choosing models by the constraints that actually matter — cost, latency, task type, model preference — instead of a single leaderboard number. We'll demo a sub-200ms routing decision running on a purpose-built 30B MoE model with no application code changes, walk through real coding workflows routing most traffic to open models without losing accuracy, and show where this goes next: evals, caching, and personalization.
Media Evidence
No related AI Engineer channel video found yet.
Evidence Graph
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Media Signals
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Agent Reading Notes
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Transcript Status
No official session recording transcript was found by exact title match on the AI Engineer YouTube channel during this run.
People
Notes
- Pending transcript synthesis when an official recording or confirmed matching video is available.
Synthesis
Synthesized Breakdown
Preferences > Benchmarks: Model Routing for How Teams Actually Build ## Conference Context - Date/time: 2026-07-01 · 12:05pm-12:25pm - Track/room: AI Architects: AI Factories · Leadership 2 - Speaker(s): Archana Kamath, Tyler Gillam - Session type/status: session · confirmed - Track: AI Architects: AI Factories - Room: Leadership 2 - Session type: session - Status: confirmed ## Session Description There is no best model. There's only the right model for a given task, and the right model depends on your team's preferences, not a benchmark score. This talk makes the case for preference-aligned routing: choosing models by the constraints that actually matter — cost, latency, task type, model preference — instead of a single leaderboard number. We'll demo a sub-200ms routing decision running on a purpose-built 30B MoE model with no application code changes, walk through real coding workflows routing most traffic to open models without losing accuracy, and show where this goes next: evals, caching, and personalization.
Speaker And Company Context
- Archana Kamath — VP of Engineering at Digital Ocean.
- Tyler Gillam — Senior Software Engineer II - Agentic AI at Digital Ocean.
Topics Covered
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.