MELT (Metrics, Events, Logs, Traces)

MELT is the framework that defines the four foundational data types of modern observability: Metrics (numeric measurements over time), Events (discrete state changes), Logs (textual records of system behavior), and Traces (request paths across distributed systems).

MELT replaced earlier, narrower framings of observability—which often focused on just metrics and logs—as distributed systems made request-level tracing and event-driven architectures more central to operations. Each MELT pillar answers a different question. Metrics tell you how much and how fast (request rate, error rate, latency percentiles). Logs tell you what happened (errors, state changes, application output). Traces tell you where (the path a request took across services, where time was spent, where it failed). Events tell you when something changed (deployments, configuration updates, feature flag flips). Together they form the observable surface of a modern system.

The framework matters because most production incidents require correlating across at least three of the four pillars to diagnose. A latency spike (metrics) requires log investigation (logs) to find the failing component, trace analysis (traces) to find where time was spent, and deployment history (events) to identify the recent change that triggered it. Tools that handle one or two pillars in isolation force engineers to context-switch between platforms during investigation, one of the largest hidden costs in modern incident response. The 2025 Grafana Observability Survey found that observability now consumes 17% of total compute infrastructure spend on average, with much of that cost driven by separately-managed pillars.

AI SRE captures MELT as input but operates above it. Rather than asking the engineer to manually correlate across pillars, an AI SRE traverses all four simultaneously inside the Production World Model™, surfacing the cross-pillar evidence chain that supports a given hypothesis.