Logs are where Datadog bills grow and where investigations succeed, usually in that order. The platform's log management is genuinely good, but its defaults assume you want everything ingested, indexed, and retained, and that assumption is expensive. Running logs well in Datadog means deciding, deliberately, what to collect, what to index, and what to keep.
Collection comes first: configuring log collection through the agent and integrations, then setting up log management end to end: pipelines, indexes, exclusion filters and archives, in the order that keeps costs controlled. Parsing is where most setups fray: log parsing errors have a small set of recurring causes, and fixing them is what makes everything downstream searchable.
Once logs flow, the payoff is in how you use them. Log search syntax is a fifteen-minute investment that changes how fast you investigate. Automated log analysis surfaces patterns without anyone tailing logs, and correlating logs with metrics is the difference between a symptom and a cause.
Then the money: ingestion controls decide what you pay for, retention settings decide for how long, and archives keep the long tail queryable without keeping it indexed. If you operate under compliance obligations, data retention policies turn regulatory requirements into configuration. In our own managed environments, tuning exactly these controls is how we deliver a 77% log volume reduction without losing investigative capability.