| File Name: | LLM Observability and Cost Management: Langfuse, Monitoring |
| Content Source: | https://www.udemy.com/course/llm-observability-cost/ |
| Genre / Category: | Other Tutorials |
| File Size : | 1.8 GB |
| Publisher: | udemy |
| Updated and Published: | January 24, 2026 |
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
DOWNLOAD LINK: LLM Observability and Cost Management: Langfuse, Monitoring
LLM_Observability_and_Cost_Management_Langfuse_Monitoring.part1.rar – 1000.0 MB
LLM_Observability_and_Cost_Management_Langfuse_Monitoring.part2.rar – 810.2 MB
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