LLM Observability and Cost Management: Langfuse, Monitoring

LLM Observability and Cost Management: Langfuse, Monitoring
Last updated 1/2026
Duration: 2h 36m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 1.77 GB

Production-Ready LLM Monitoring with Langfuse, Cost Optimization, Tracing, Alerting & Real-World Debugging Patterns

What you’ll learn
– 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
– Build production-ready code patterns for token tracking, cost calculation, and PII redaction
Requirements
– 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

Description
Are you spending too much on LLM API costs? Do you struggle to debug production AI applications?

This course teaches you how to implement professional-grade observability for your LLM applications – and cut your AI costs by 50-80% in the process.

The Problem:

– A single runaway prompt can cost $10,000 in an afternoon

– Token usage spikes 300% and no one knows why

– Users complain about slow responses, but you can’t identify the bottleneck

– Your RAG pipeline retrieves garbage, and the LLM hallucinates confidently

The Solution:

This course gives you the tools, patterns, and code to monitor, debug, and optimize every LLM call in your stack.

What You’ll Build:

– Production-ready observability pipelines with Langfuse

– Semantic caching systems that reduce costs by 30-50%

– Alert systems that catch cost spikes before they become budget crises

– Debug workflows that identify issues in minutes, not hours

What Makes This Course Different:

1.Cost-First Approach- We lead with ROI, not just monitoring theory

2.Vendor-Neutral -Compare Langfuse, LangSmith, Arize, Helicone objectively

3.Production-Grade- Skip the basics, dive into real-world patterns

4.Hands-On Code- Every concept includes working Python code you can deploy today

Course Structure:

– Module 1: The Business Case – Why Observability = Money

– Module 2: Understanding LLM Costs – Where Your Money Goes

– Module 3: Observability Platform Selection – Choosing the Right Tool

– Module 4: Instrumenting Your LLM Application – Hands-On Implementation

– Module 5: Cost Optimization Strategies That Work – Caching, Routing, Prompts

– Module 6: Monitoring, Alerting & Debugging – Production Operations

– Module 7: Production Patterns & Security – Enterprise-Ready Implementation

Real Results:

Teams implementing these patterns typically see:

– 50-80% reduction in LLM API costs

– 80% faster debugging with proper tracing

– ROI of 7-30x on observability investment

Who This Course Is For:

– ML Engineers & AI Engineers running LLMs in production

– Backend developers building LLM-powered features

– Tech leads responsible for AI infrastructure costs

– Anyone paying for OpenAI, Anthropic, or other LLM APIs

Prerequisites:

– Basic Python programming experience

– Familiarity with LLM APIs (OpenAI, Anthropic, etc.)

– No prior observability experience required

Enroll now and take control of your AI costs.

Who this course is for:
– ML Engineers and AI Engineers who run LLM applications in production and need to control costs
– Backend developers building features powered by OpenAI, Anthropic, or other LLM providers
– Tech leads and engineering managers responsible for AI infrastructure budgets
– Python developers who want to add observability to their existing LLM projects
– Anyone paying for LLM API calls who wants to understand where their money goes
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