Telemetry-Driven Quality Metrics for LLM Deployments in Cloud Infrastructure

Abstract

The paper explores telemetry-based quality metrics for large language models on cloud infrastructure. Compute nodes and storage layers can provide run-time observability on a fine- grained level through Telemetry data. This paper applies a systematic measurement model for latency, throughput, error rates and resource usage. Measurements are exposed via distributed tracing, structured logging and high-frequency telemetry streaming. Time series databases and observability pipelines consolidate metrics that are used for real-time anomaly detection and predictive workload modeling. Consolidated, consistent patterns for data collection and correlation are offered by cloud-native monitoring stacks such as Prometheus or Open Telemetry. Model-level telemetry tokens generation latency, context windows utilization, GPU memory consumption and parameters loading time. Quality evaluation integrates SLA conformance, benchmarks from reliability engineering, and limits of scalability. These dynamic autoscaling policies rely on some of the telemetry metrics for orchestrator-driven load balancing and fault tolerance. It uses online profiling to recognize hardware bottlenecks and make the best use of accelerators in heterogeneous clusters. Shadow deployments and canary rollouts use telemetry to examine inference quality before releasing into production. Metric dashboards display the health of deployments across multiple regions, how well they fail over between zones, and how resilient they are when subjected to traffic surges. The paper employs telemetry-driven governance, which finds that operational compliance, security monitoring, and cost optimization also become self-aware in operations. This policy framework provides higher resilience and efficiency, along with transparent lifecycle management at an enterprise scale for LLM workloads.