Building AI features in Flutter? Meet 1:1 with Flutter GDEs to discuss running local models on mobile devices, building goal-driven agents with Genkit, rendering safe dynamic screens with GenUI, and monitoring LLM latency and token costs in production.

Want to grow beyond writing day-to-day UI code? Visit the Flutter GDE booth for 1:1 mentorship on moving from developer to tech lead or architect, making strong technical decisions, validating startup ideas, and shipping your own indie products with Flutter and AI.

Most Flutter apps today are LLM-powered, and it's often just a few steps to bring an agent into the mix. But once your app starts calling models like Gemini or OpenAI, debugging becomes fundamentally different. Unlike traditional APIs, LLMs are non-deterministic, making it difficult to understand why responses change or costs increase. This talk explores practical ways to add telemetry and observability to AI-powered Flutter apps (that is using cloud backends like Supabase). Some of the things we'll cover: - Which AI metrics matter: cost, latency (and why percentiles beat averages), token usage, errors, and output quality - How telemetry helps you monitor, debug, and improve LLM-powered features - Hosted AI observability platforms like Langfuse: tracing, evaluations, dashboards, pricing, and alternatives By the end, you'll have a practical framework for monitoring, debugging, and improving your LLM-powered features.