Large Language Models (LLMs) have emerged as powerful tools across various industries, but their non-deterministic nature brings unique monitoring challenges. Real-time LLM monitoring is the continuous analysis of model outputs as they're generated, enabling immediate detection and response capabilities. This approach integrates directly with your LLM inference pipeline, creating a streaming data architecture that captures outputs, analyzes them, and potentially triggers alerts or interventions within milliseconds or seconds. In contrast, batch LLM monitoring is the scheduled collection and analysis of model interactions over defined time periods, accumulating data for hours, days, or even weeks before conducting comprehensive analyses. Batch systems can detect subtle patterns that might not be apparent in individual interactions, but often provide more accurate analysis at the cost of timeliness. The choice between real-time and batch LLM monitoring approaches depends on application needs, with real-time systems exceling at detecting critical issues related to AI safety and reliability, while batch systems excel at identifying pattern recognition and comprehensive analysis capabilities. Real-time monitoring demands constant computational resources and requires dedicated infrastructure that can handle peak loads without introducing significant latency to user experiences, whereas batch systems use more traditional ETL pipelines and data warehousing solutions. Ultimately, the effectiveness of these different feedback loops depends on application needs, with real-time monitoring suitable for mission-critical applications and batch monitoring better suited for continuous quality improvement. A unified platform that combines the strengths of both methods can provide the best results.