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Observability Summit 2026

Event artwork for Observability Summit 2026

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About this event

What to expect

The Observability Summit — 2026 is a full-day technical gathering bringing together the Toronto observability ecosystem for deep dives into modern monitoring, reliability engineering, and LLM infrastructure. Scheduled for April 16th, 2026, this summit is designed for practitioners building and operating complex AI systems at scale, from GPU cloud operators to platform engineers debugging production failures.

What You'll Learn

This summit tackles three critical areas of modern observability:

Neo-Cloud Infrastructure Observability explores the unique challenges facing next-generation cloud providers and GPU infrastructure platforms. Sessions will cover metrics volume management, cost control strategies, multi-tenant isolation, and reliability patterns for high-throughput environments. Expect real-world war stories about what actually works when operating control planes at scale.

LLM Observability and Inference Engineering addresses the new failure modes introduced by AI-powered applications. Topics include latency and throughput monitoring for LLM inference, prompt quality tracking, RAG pipeline visibility (retrieval quality, embedding performance, cache hit rates), and debugging modern LLM stacks built on vLLM, TGI, and Triton. You'll learn practical approaches to detecting hallucinations, monitoring token-level metrics, and optimizing GPU utilization.

Core Cloud Observability covers essential Kubernetes monitoring patterns, application performance monitoring (APM), distributed tracing, and incident response workflows. Sessions will explore OpenTelemetry implementations, metrics/logs/traces pipelines, cost-aware observability strategies, and practical alerting approaches that reduce noise without missing critical signals.

Who Should Attend

This summit is ideal for platform engineers, SREs, DevOps teams, observability architects, AI/ML engineers running production LLM applications, founders building observability tools, and operators managing GPU cloud infrastructure. Whether you're debugging autoscaling behavior in Kubernetes or optimizing inference latency for production AI agents, you'll find relevant technical content and practitioners facing similar challenges.

Speaker and Demo Opportunities

The TorontoAI team is actively seeking speakers for deep technical talks, case studies, and lessons learned from production observability implementations. If you're building observability toolin

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