LLM Observability with an AI Gateway: Complete Guide

LLM observability involves monitoring requests, responses, token consumption, costs, latency, errors, fallback behavior, and unusual traffic patterns across AI applications in real time, addressing challenges that traditional application performance monitoring may not capture because LLM outputs, routing, and usage vary between requests. The passage argues that application-level SDK logging provides limited, per-application visibility, while an AI gateway positioned between applications and model providers can centralize data across teams, models, and services without requiring application changes. It identifies request latency percentiles, token usage, cost attribution, error rates, fallback rates, and anomaly detection as key production metrics, emphasizing that aggregate analysis can reveal provider issues, inefficient prompts, runaway agents, and unexpected spending. It also presents OpenTelemetry as a common tracing standard for integrating AI telemetry with existing observability platforms, and describes NeuralTrust’s TrustGate gateway and TrustLens posture-management product as tools intended to provide request-level monitoring and longer-term agent behavior analysis.

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