What Nobody Tells You About Running AI in Production
Learn how teams run AI in production, control LLM costs, evaluate AI outputs, trace agents, and catch failures traditional monitoring can miss.
August 18, 2026
01:00 AM ET
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What you'll learn
How to run AI applications in production with the right architecture, infrastructure, guardrails, and operational foundations.
How to control LLM and AI costs using model selection, token management, prompt caching, and model routing.
How to monitor and evaluate AI applications and agents using observability, tracing, task-level evaluation, and signals that catch failures traditional monitoring can miss.
Running AI applications in production requires more than choosing the right model. An AI system can have healthy uptime, latency, and error rates while still producing the wrong results. Teams need the right architecture, cost controls, evaluation, and observability to understand not only whether an AI application is running, but whether it is working correctly.
Hear real-world lessons from running AI in production, including how teams control LLM token costs, choose and route models, evaluate AI outputs, trace agents and tool calls, and troubleshoot failures that traditional application monitoring and APM may not detect.