10 Deploying and Monitoring
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.
10.1 Introducing LLMOps
10.2 Serving LLMs: Hosted APIs vs. open-source models
10.2.1 Using hosted APIs
10.2.2 The open-source alternative
10.2.3 The hybrid solution: Best of both worlds
10.3 Building LLM-native monitoring systems
10.3.1 What really matters: The four questions
10.3.2 Logging what actually matters
10.3.3 Setting up alerts that actually help
10.3.4 Catching cost explosions before they hurt
10.3.5 Building dashboards that drive action
10.3.6 Output quality monitoring
10.4 User experience and feedback monitoring
10.4.1 Explicit feedback collection
10.4.2 Implicit feedback signals
10.4.3 Building actionable feedback loops
10.5 Ensuring high-quality outputs in production
10.5.1 The three-pillar quality framework
10.5.2 Prompt engineering for consistent quality
10.5.3 Continuous quality monitoring with automated testing
10.6 Observability in practice: Introducing Langfuse with a real-world case study
10.6.1 Case Study: How Huntr uses Langfuse to power the AI Resume Builder
10.7 Summary