Industrial predictive maintenance built on large language models (LLMs) combines sensor fusion, time-series analysis, and contextual reasoning to detect degradation and predict failures. This topic covers how LLMs are integrated into the full stack—data ingestion and orchestration, cataloging and lineage, model monitoring, and deployment models from cloud to on-device inference—and how those choices affect prediction accuracy, latency, and operational risk. Relevance in late 2025 stems from wider adoption of multimodal LLMs for interpreting complex sensor and maintenance logs, increasing expectations for real-time edge decisioning, and growing use of standardized interfaces (e.g., Model Context Protocol servers) that let LLMs access databases, telemetry and observability tooling securely. Key tools and roles: Dagster for building and orchestrating reliable data pipelines; Arize Phoenix’s MCP server for unified tracing, evaluation and experiment access to support model validation and drift detection; MCP Toolbox for Databases to simplify secure DB connectivity for LLMs; Supabase’s MCP server to enable LLMs to read/write project data and run edge functions; and Vizro-MCP for producing validated dashboards from LLM-derived insights. Practical comparisons center on accuracy (data quality, sensor coverage, model retraining cadence), sensor integrations (protocol adapters, pre-processing, timestamp alignment), and deployment models (cloud compute for heavy training and ensemble scoring vs on-device inference for low-latency safety actions). Other critical considerations are data lineage and governance, end‑to‑end orchestration, and monitoring pipelines to maintain prediction fidelity in production. Understanding these trade-offs enables engineers to choose architectures that balance prediction performance, operational resilience, and compliance.