Datadog Database Monitoring can surface enormous detail — and bill for it. The skill is choosing the few signals that answer real cost and reliability questions, and not paying to collect noise nobody acts on.
Token spend behaves differently from compute and storage — it scales with usage and prompt design. Treating it like an engineering cost line, the way you treat a database bill, is how you bring it under control.
The skills that make a good cost-aware DBA — measuring usage, finding structural waste, balancing cost against reliability — transfer almost directly to AI workloads. Database engineers are unusually well positioned to own AI cost.
A practitioner walkthrough of the review method: what to look at, in what order, how to quantify an opportunity honestly, and how to turn findings into a prioritized 30/60/90-day plan.
Aurora cost hides in places the console doesn't foreground — I/O charges, oversized writers and readers, replica sprawl, and storage. A structured way to find and reduce each without hurting reliability.
Table and index bloat and unused indexes are well-known Postgres problems — and direct cloud-cost problems: wasted storage, write amplification, and extra I/O. How to measure both with read-only queries and remediate safely.