Production-Ready LLM Monitoring with Langfuse, Cost Optimization, Tracing, Alerting & Real-World Debugging Patterns
Are you spending too much on LLM API costs? Do you struggle to debug production AI applications?
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.
Course Content
- Introduction –> 2 lectures • 1min.
- The Business Case Why Observability = Money –> 5 lectures • 10min.
- Understanding LLM Costs – Where Your Money Goes –> 3 lectures • 19min.
- Observability Platform Selection – Langfuse and Hands-on –> 6 lectures • 34min.
- Instrumenting Your LLM Application –> 3 lectures • 52min.
- Cost Optimization Strategies That Work –> 5 lectures • 24min.
- Monitoring, Alerting & Debugging –> 1 lecture • 8min.
- Production Patterns & Security –> 2 lectures • 6min.
- Wrap up and Next Steps –> 1 lecture • 2min.
Requirements
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 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
Stop flying blind with your LLM applications. Start monitoring, optimizing, and saving money today.
Enroll now and take control of your AI costs.