LLM Observability and Cost Management: Langfuse, Monitoring
Production-Ready LLM Monitoring with Langfuse, Cost Optimization, Tracing, Alerting & Real-World Debugging Patterns
Lo que serás capaz de hacer
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.
- 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
Temario
UNIDAD 1 - Introduction · 2 leccionesUNIDAD 1 - Introduction
UNIDAD 1 - Introduction
- Introduction
- Get Course Code
UNIDAD 2 - The Business Case Why Observability = Money · 6 leccionesUNIDAD 2 - The Business Case Why Observability = Money
UNIDAD 2 - The Business Case Why Observability = Money
- Observability and Cost Management - Overview
- The Hidden Costs of LLM Applications
- Traditional vs LLM Observability
- The Three Pillars for LLMs
- ROI Calculator - Making the Business Case
- Section 2: Business Case for LLM Observability
UNIDAD 3 - Understanding LLM Costs - Where Your Money Goes · 4 leccionesUNIDAD 3 - Understanding LLM Costs - Where Your Money Goes
UNIDAD 3 - Understanding LLM Costs - Where Your Money Goes
- Understanding LLM Costs
- Where Costs Hide - RAG and Agent Pipeline
- The Hidden Cost Multiplier
- Section 3: Understanding LLM Costs
UNIDAD 4 - Observability Platform Selection - Langfuse and Hands-on · 7 leccionesUNIDAD 4 - Observability Platform Selection - Langfuse and Hands-on
UNIDAD 4 - Observability Platform Selection - Langfuse and Hands-on
- Observability Platform Selection
- Setting Up Langfuse
- Setting up Langfuse and Creating First Trace
- Langfuse Data Model
- Hands-on: First LLM Trace - Deep Dive
- Langfuse API Levels - Code Demonstrations
- Section 4: Observability Platform Selection - Langfuse and Hands-on
UNIDAD 5 - Instrumenting Your LLM Application · 3 leccionesUNIDAD 5 - Instrumenting Your LLM Application
UNIDAD 5 - Instrumenting Your LLM Application
- Production Instrumented LLM Use Case - Hands-on
- Instrumenting a Multi-Step RAG Pipeline - Langfuse Observability - Full Handson
- Framework Integration (LangChain)
UNIDAD 6 - Cost Optimization Strategies That Work · 5 leccionesUNIDAD 6 - Cost Optimization Strategies That Work
UNIDAD 6 - Cost Optimization Strategies That Work
- Cost Optimization Strategies - Overview
- Prompt Optimization - Handson
- Semantic Caching
- Smart Model Routing
- Cost Optimization Summary
UNIDAD 7 - Monitoring, Alerting & Debugging · 1 leccionesUNIDAD 7 - Monitoring, Alerting & Debugging
UNIDAD 7 - Monitoring, Alerting & Debugging
- Setting up Alerts that Matter
UNIDAD 8 - Production Patterns & Security · 2 leccionesUNIDAD 8 - Production Patterns & Security
UNIDAD 8 - Production Patterns & Security
- Security and Compliance Patterns
- Production Patterns Implementations - Real-world
UNIDAD 9 - Wrap up and Next Steps · 2 leccionesUNIDAD 9 - Wrap up and Next Steps
UNIDAD 9 - Wrap up and Next Steps
- Course Recap and Next Steps
- Bonus
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
9 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
3 cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
Muestra · verificable por folio y QR
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 9 unidades · 32 lecciones · 45 h
- 12 meses de acceso
- 3 cuestionarios de evaluación
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.
