9 Deploying and monitoring large language models for high-quality outcomes

 

This chapter covers

  • How LLMOps differs from traditional software operations
  • Choosing between hosted APIs and self-hosted models
  • Building hybrid deployment architectures that optimize for both cost and capability
  • Implementing LLM-native monitoring systems that track response quality, user satisfaction, and business impact
  • Designing automated quality assurance pipelines to maintain output standards at scale

At 3:04 AM, an alert arrives that no one wants to see:

“URGENT: AI chatbot billing alert – $47,000 this month. System failing.”

Just days before, the company’s new LLM-powered support assistant had been a success story in the making. It sailed through internal testing, impressed executives, and promised to reduce support costs dramatically. Now it’s producing unpredictable results, racking up massive expenses, and creating more confusion than value.

This kind of breakdown is increasingly common. A model that performs flawlessly in development can collapse in production—not because the technology is broken, but because the surrounding system wasn’t designed for real-world complexity. Language models aren’t traditional software. Their behavior shifts based on prompts, context quality, system load, user phrasing, and model updates. Without proper architecture, observability, and monitoring, they quietly fail in ways that are hard to detect and expensive to ignore.

9.1 Introducing LLMOps

9.2 Serving LLMs: Hosted APIs vs. open-source models

9.2.1 Using hosted APIs

9.2.2 The open-source alternative

9.2.3 The hybrid solution: Best of both worlds

9.3 Building LLM-native monitoring systems

9.3.1 What really matters: The four questions

9.3.2 Logging what actually matters

9.3.3 Setting up alerts that actually help

9.3.4 Catching cost explosions before they hurt

9.3.5 Building dashboards that drive action

9.3.6 Output quality monitoring

9.4 User experience and feedback monitoring

9.4.1 Explicit feedback collection

9.4.2 Implicit feedback signals

9.4.3 Building actionable feedback loops

9.5 Ensuring high-quality outputs in production

9.5.1 The three-pillar quality framework

9.5.2 Prompt engineering for consistent quality

9.5.3 Continuous quality monitoring with automated testing

9.6 Observability in practice: Introducing Langfuse with a real-world case study

9.6.1 Case Study: How Huntr uses Langfuse to power the AI Resume Builder

9.7 Summary