Log Aggregation

In distributed architectures, a single user request may touch dozens of services, each generating its own logs. Without aggregation, debugging requires manually searching log files across multiple servers. Log aggregation pipelines collect logs from all sources, parse and structure them, enrich them with metadata like trace IDs, and index them for fast search and analysis.

For AI product teams, log aggregation is essential for debugging AI behavior issues that span multiple services. When a user reports a poor recommendation, the team needs to trace the request through the API gateway, feature retrieval service, model inference endpoint, and response formatting service. Structured logging with correlation IDs makes this tracing possible. Growth teams use aggregated logs to analyze experiment-related events, track funnel progression, and investigate anomalies in user behavior data. Log-based metrics complement traditional metrics by enabling ad-hoc analysis: when a dashboard shows an unexpected spike, logs provide the detail needed to understand why. The cost of log storage grows with traffic, so teams should implement log level management and retention policies that balance investigation needs with budget constraints.