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.