Observability for LLM Applications: Cost, Quality, and Reliability

​As LLM applications move from prototype to production, teams face a new set of operational challenges: unpredictable costs, hard-to-detect quality regressions, and reliability failures that don't show up until they're impacting users.

​This webinar provides a practical, production-readiness framework for LLM applications, covering:

• ​Cost: Token monitoring, per-request cost tracking, and call optimization strategies, including the trade-offs between evaluation depth and monitoring cost

• ​Quality: How to measure response quality through evals and drift detection, plus continuous quality metrics like faithfulness, hallucination rate, and answer relevance

• ​Context as a Quality Lever: How context engineering (context layers, contextual retrieval) improves output quality without requiring larger models

• ​Reliability: Fallback strategies, rate limit handling, and error handling patterns that preserve user experience when model reliability degrades

• ​Instrumentation & Traceability: How to instrument LLM pipelines with distributed tracing, and how MCP acts as an observability/control layer for tracing which context and tools a model uses

​The session closes with a live demo of an end-to-end observability stack, including an overview of the tech stack used.