Description
LLM Observability and Cost Management: Langfuse Monitoring is a course on how to monitor, troubleshoot, evaluate, and optimize LLM-based applications published by Udemy Online Academy. The course focuses on the unique challenges of generative AI systems, including token consumption, API costs, latency, RAG pipeline performance, model behavior, and hard-to-trace failures. Learners work with Langfuse to instrument LLM applications, build traces and spans, capture metadata, monitor token consumption and costs, and analyze multi-stage workflows. The course starts with a simple problem that every team working with LLM eventually faces: When an application moves beyond the demo stage, you need real-world visibility into what it’s actually doing — not just whether it’s returning a response, but why it took that path, how much it cost, and where it’s slow or wrong. You learn to instrument applications with Langfuse and build traces and spans that make multi-stage LLM workflows, including RAG pipelines, truly auditable, not obscure.
From there, the course moves on to cost, one of the most tangible pain points of LLM systems in production. You learn practical techniques for reducing API cost by 50-80% through semantic caching, smarter model routing, and prompt optimization, along with how to set up cost alerts and monitoring dashboards that catch budget issues early, not after a surprise bill. Troubleshooting gets equal attention: Using traces, spans, and the right instrumentation patterns, you learn to go from “something is wrong” to the root cause in minutes, not hours of guessing. In the end, you will have a repeatable observability setup that you can apply to any LLM application, giving you the visibility and cost control you need to responsibly deploy productive AI systems in production.
What you will learn in LLM Observability and Cost Management: Langfuse Monitoring:
- Implement production-grade LLM observability using Langfuse and understand tracing concepts
- Reduce LLM API costs by 50-80% using semantic caching, model routing, and prompt optimization
- Debug LLM applications in minutes using traces, spans, and proper instrumentation patterns
- Set up cost alerts and monitoring dashboards that catch budget issues before they escalate
- and …
Course specifications
Publisher: Udemy
Instructors: Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor
Language: English
Level: Introductory to Advanced
Number of Lessons: 29
Duration: 2 hours and 35 minutes
Course topics
LLM Observability and Cost Management: Langfuse Monitoring Prerequisites
Basic Python programming skills (variables, functions, classes)
Familiarity with LLM APIs (OpenAI, Anthropic, or similar) – you should have made at least a few API calls before
A code editor (VS Code recommended) and Python 3.9+ installed
Pictures
LLM Observability and Cost Management: Langfuse Monitoring introduction video
Installation guide
After Extract, watch with your favorite Player.
Subtitle: None
Quality: 1080p
Downloadly link
Rapidgator link
File password (s): www.downloadly.ir
Size
1.8 GB