LLMs, GPUs, and Everything in Between: End-to-End Observability For Your Entire AI Stack

As generative AI apps move from prototype to production, teams often face a widening observability gap. The complexity of the GenAI stack introduces blind spots that can impact performance, drive up costs, and erode user trust. 

In this talk, we’ll show how modern observability helps you build performant, secure, and cost-efficient GenAI applications—connecting insights across every layer of your stack, from infrastructure and GPUs to retrieval pipelines and LLM output.

You’ll learn how to:

• Trace latency, errors, and token usage across your LLM apps and agents

• Evaluate and detect hallucinations, prompt injections, and PII leaks

• Iterate faster and confidently deploy changes to your LLM apps 

• Detect underutilized GPUs by pod, workload, and device

• Optimize GPU provisioning with key metrics

We’ll share real-world examples from customers who’ve cut debugging time, reduced GPU spend, and stopped bad model outputs from reaching production.