LLM Observability and Cost Management: Langfuse, Monitoring

<div>Are you spending too much on LLM API costs? Do you struggle to debug production AI applications?&nbsp;<span style="font-size: 1rem;">This&nbsp;</span><b><u>LLM Observability and Cost Management: Langfuse, Monitoring&nbsp;</u></b><span style="font-size: 1rem;"><b><u>course</u></b> teaches you how to implement professional-grade observability for your LLM applications — and cut your AI costs by 50-80% in the process.</span></div><div><br></div><div>The Problem:</div><div><ul><li><span style="font-size: 1rem;">A single runaway prompt can cost $10,000 in an afternoon</span></li><li><span style="font-size: 1rem;">Token usage spikes 300% and no one knows why</span></li><li><span style="font-size: 1rem;">Users complain about slow responses, but you can't identify the bottleneck</span></li><li><span style="font-size: 1rem;">Your RAG pipeline retrieves garbage, and the LLM hallucinates confidently</span></li></ul></div><div><span style="font-size: 1rem;">The Solution:</span></div><div><ul><li><span style="font-size: 1rem;">This course gives you the tools, patterns, and code to monitor, debug, and optimize every LLM call in your stack.</span></li></ul></div><div><span style="font-size: 1rem;">What You'll Build:</span></div><div><ul><li><span style="font-size: 1rem;">Production-ready observability pipelines with Langfuse</span></li><li><span style="font-size: 1rem;">Semantic caching systems that reduce costs by 30-50%</span></li><li><span style="font-size: 1rem;">Smart model routing that automatically selects the cheapest model for each task</span></li><li><span style="font-size: 1rem;">Alert systems that catch cost spikes before they become budget crises</span></li><li><span style="font-size: 1rem;">Debug workflows that identify issues in minutes, not hours</span></li></ul></div><div><span style="font-size: 1rem;">What Makes This Course Different:</span></div><div><span style="font-size: 1rem;">1. Cost-First Approach — We lead with ROI, not just monitoring theory</span></div><div><span style="font-size: 1rem;">2. Vendor-Neutral — Compare Langfuse, LangSmith, Arize, Helicone objectively</span></div><div><span style="font-size: 1rem;">3. Production-Grade — Skip the basics, dive into real-world patterns</span></div><div><span style="font-size: 1rem;">4. Hands-On Code — Every concept includes working Python code you can deploy today</span></div><div><span style="font-size: 1rem;">Course Structure:</span></div><div><ul><li><span style="font-size: 1rem;">Module 1: The Business Case — Why Observability = Money</span></li><li><span style="font-size: 1rem;">Module 2: Understanding LLM Costs — Where Your Money Goes</span></li><li><span style="font-size: 1rem;">Module 3: Observability Platform Selection — Choosing the Right Tool</span></li><li><span style="font-size: 1rem;">Module 4: Instrumenting Your LLM Application — Hands-On Implementation</span></li><li><span style="font-size: 1rem;">Module 5: Cost Optimization Strategies That Work — Caching, Routing, Prompts</span></li><li><span style="font-size: 1rem;">Module 6: Monitoring, Alerting &amp; Debugging — Production Operations</span></li><li><span style="font-size: 1rem;">Module 7: Production Patterns &amp; Security — Enterprise-Ready Implementation</span></li></ul></div><div><span style="font-size: 1rem;">Real Results:</span></div><div><span style="font-size: 1rem;">Teams implementing these patterns typically see:</span></div><div><ul><li><span style="font-size: 1rem;">50-80% reduction in LLM API costs</span></li><li><span style="font-size: 1rem;">80% faster debugging with proper tracing</span></li><li><span style="font-size: 1rem;">ROI of 7-30x on observability investment</span></li></ul></div><div><span style="font-size: 1rem;">Who This Course Is For:</span></div><div><ul><li><span style="font-size: 1rem;">ML Engineers &amp; AI Engineers running LLMs in production</span></li><li><span style="font-size: 1rem;">Backend developers building LLM-powered features</span></li><li><span style="font-size: 1rem;">Tech leads responsible for AI infrastructure costs</span></li><li><span style="font-size: 1rem;">Anyone paying for OpenAI, Anthropic, or other LLM APIs</span></li></ul></div><div><span style="font-size: 1rem;">Prerequisites:</span></div><div><ul><li><span style="font-size: 1rem;">Basic Python programming experience</span></li><li><span style="font-size: 1rem;">Familiarity with LLM APIs (OpenAI, Anthropic, etc.)</span></li><li><span style="font-size: 1rem;">No prior observability experience required</span></li></ul></div><div><span style="font-size: 1rem;">Stop flying blind with your LLM applications. Start monitoring, optimizing, and saving money today.</span></div><div><br></div><div>Enroll now and take control of your AI costs.</div>

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