Uncontrolled LLM usage can silently drain budgets and slow down production systems. This course equips you with practical tracing, instrumentation, and cost analysis techniques to gain full visibility into your AI stack. Learn to detect bottlenecks, control token spending, and operate reliable, cost-efficient LLM applications at scale.
Production LLM systems introduce unpredictable token usage, hidden RAG multipliers, and debugging blind spots that traditional monitoring cannot solve. This course begins by building the business case for observability, demonstrating how tracing and cost transparency directly impact ROI. You will explore how LLM costs accumulate, where money leaks inside pipelines, and why traditional observability models fall short for generative AI workloads. The journey then moves into platform evaluation and hands-on implementation. You will set up Langfuse, understand its data model, create traces, and instrument multi-step RAG workflows. Framework integrations such as LangChain are covered to show how real production systems capture spans, metadata, and token usage. Each step transforms abstract monitoring theory into practical, deployable code patterns. In the final sections, the focus shifts to optimization and operational excellence. You will implement prompt tuning, semantic caching, smart model routing, cost alerts, and monitoring dashboards. The course concludes with security patterns, PII redaction strategies, and enterprise-ready production practices, ensuring you leave with a complete observability and cost governance framework. This course is designed for ML Engineers, AI Engineers, backend developers, and technical leads responsible for deploying and maintaining LLM-powered systems in production. It is particularly valuable for professionals managing API budgets, RAG pipelines, or multi-step agent workflows. Python developers familiar with OpenAI, Anthropic, or similar APIs will benefit from learning how to introduce structured tracing, cost controls, monitoring dashboards, and secure production patterns into their existing AI applications. The course follows a structured path from business fundamentals to deep technical implementation. It begins with cost visibility concepts, progresses through hands-on platform setup and tracing instrumentation, and culminates in optimization, alerting, and security patterns. Each module reinforces practical deployment with real production scenarios and executable Python examples. This course is based on Production LLM Monitoring: Observability, Tracing & Cost Optimization, by Paulo Dichone. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.
