Teams often adopt flat-rate pricing (slot reservations) to stabilize costs or optimize for heavy, recurring workloads. However, if query volumes drop — due to seasonal cycles, architectural shifts (e.g., workload migration), or inaccurate forecasting — those reserved slots may sit underused. This inefficiency is easy to miss, as the cost remains fixed and detached from usage volume. Unlike autoscaling models, reservations require active monitoring and manual adjustment. In some organizations, multiple projects reserve separate slot pools, exacerbating waste through fragmentation.
BigQuery offers two primary billing models: * **On-Demand:** Billed per TB of data scanned. * **Flat-Rate Reservations:** Billed based on dedicated slots reserved, regardless of actual usage. Flat-rate pricing is ideal for consistent, high-volume workloads. However, when workload patterns are unpredictable or seasonal, reserved slots may remain idle, generating cost without performance benefit. Slot commitments are billed per second with a minimum duration, and unused capacity is not refunded or reallocated automatically.
