Production LLM Monitoring: Observability, Tracing & Cost Optimization
Mastering LLM Observability Tracing and AI Cost Control in Production Systems
Created by Paulo Dichone
Gain the skills to monitor and optimize large language model systems in production. Discover how to track token usage, spot bottlenecks, and manage costs so your AI applications stay reliable and efficient. Learn practical techniques that help you keep your LLM-powered solutions running smoothly at scale.
Packt | Feb 2026 | 155 min
What You Will Learn
You will start by exploring the business reasons for LLM observability and cost control. Step by step, you will set up monitoring platforms, instrument real-world AI workflows, and analyze cost data. Through hands-on coding exercises and practical examples, you will learn to implement optimization strategies and secure your production systems.
Key Features
- Set up tracing and observability tools to monitor LLM performance and costs
- Instrument RAG workflows to detect inefficiencies and control token usage
- Apply prompt tuning, caching, and cost alerts to optimize AI system operations
Target Audience
Ideal for ML engineers, AI engineers, backend developers, and technical leads working with LLM-powered applications. If you manage AI API budgets, RAG pipelines, or multi-step agent workflows and want to improve reliability and cost efficiency, you will find actionable strategies and tools here. Some Python and API experience is recommended.
