Why LLM Observability Has Become Non-Negotiable Running large language models in production without observability is like flying a plane without instruments. Traditional application monitoring captures HTTP status codes and response times, but it completely misses the failure modes unique to LLM systems: hallucinated outputs that look perfectly valid, silent cost overruns from token-heavy prompts, degraded retrieval quality in RAG pipelines, and model drift that only surfaces when a customer complains. The LLM observability market has grown significantly, with Gartner predicting that by 2028, LLM observability investments will account for 50% of GenAI deployments, up from roughly 15% in early 2026. That growth reflects a real operational need. As enterprises move from one-off chatbot experiments to multi-model, multi-team architectures powering customer-facing workflows, the cost of not seeing what is happening inside your AI systems becomes existential.…