LLM Observability and Cost Management: Langfuse, Monitoring

About this course

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%- Smart model routing that automatically selects the cheapest model for each task- 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 theory2. Vendor-Neutral — Compare Langfuse, LangSmith, Arize, Helicone objectively3. Production-Grade — Skip the basics, dive into real-world patterns4. 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

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