# Inefficient use of reservations in BigQuery Canonical: https://www.pointfive.co/efficiency-hub/inefficiencies/inefficient-use-of-reservations-in-bigquery Teams often adopt capacity-based pricing (BigQuery editions reservations with baseline slots and optional commitments) to stabilize costs or optimize... By: PointFive Updated: 2026-09-28 [Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) # Inefficient Use of Reservations in BigQuery The short version Teams often adopt capacity-based pricing (BigQuery editions reservations with baseline slots and optional commitments) to stabilize costs or optimize for heavy, recurring workloads. PointFive Research Cloud cost research at PointFive GCP service [GCP BigQuery](https://www.pointfive.co/efficiency-hub/cloud-services/gcp-bigquery) Category [Databases](https://www.pointfive.co/efficiency-hub/service-category/databases) Reference CER-0069 Type Underutilized Commitment ## Explanation Why the waste happens and who it affects. However, if query volumes drop - due to seasonal cycles, architectural shifts (e.g., workload migration), or inaccurate forecasting - those baseline or committed slots may sit underused. This inefficiency is easy to miss, as the cost remains fixed and detached from usage volume. Autoscaling absorbs bursts, but baselines, commitments, and max slot settings still require active monitoring and manual adjustment. In some organizations, reservations are spread across separate administration projects, exacerbating waste through fragmentation because idle slots are shared only among reservations in the same admin project, region, and edition. ## Billing model The pricing dimensions that drive this cost. BigQuery offers two primary billing models: - On-Demand: Billed per TiB of data scanned. - Capacity-Based (Editions) Reservations: Billed for baseline slots whether or not they are used, plus autoscaled slots for the number of slots scaled rather than slots used. Capacity 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. Autoscaled slots are billed per second with a one-minute minimum by default, while optional 1-year or 3-year commitments run for their full term. Unused slots are not refunded, and idle slots are shared by default only among reservations in the same administration project, region, and edition. ## How to detect 5 checks to find it in your estate. - Review reservation utilization trends over a representative period - Compare committed slots vs. actual slots used per project or workload - Identify whether query concurrency or runtime metrics justify reservation size - Assess whether workloads could be served equally well with on-demand pricing or a lower baseline with autoscaling - Review historical query demand to identify periods of persistent underutilization ## How to fix 5 ways to remove the waste. - Reduce baseline slots, and avoid renewing oversized commitments, if sustained usage is consistently lower than committed capacity - Consolidate slot reservations across projects to improve pool utilization - Switch low-concurrency or unpredictable workloads back to on-demand pricing or to a lower baseline with autoscaling - Implement governance to review reservation adjustments regularly - Leverage the Reservation Assignment API to dynamically reassign slot pools based on usage ## Documentation Vendor references for pricing and configuration. - [Understand reservations docs.cloud.google.com](https://docs.cloud.google.com/bigquery/docs/reservations-workload-management) - [Understand slots docs.cloud.google.com](https://docs.cloud.google.com/bigquery/docs/slots) - [Introduction to workload management docs.cloud.google.com](https://docs.cloud.google.com/bigquery/docs/reservations-intro) - [BigQuery pricing cloud.google.com](https://cloud.google.com/bigquery/pricing) ## Related inefficiencies [Browse the library](https://www.pointfive.co/efficiency-hub) - GCP BigQuery CER-0295 ### [Overselecting Data and Misusing LIMIT for Cost Control in BigQuery](https://www.pointfive.co/efficiency-hub/inefficiencies/overselecting-data-and-misusing-limit-for-cost-control-in-bigquery) Analysts use SELECT \* (reading more columns than needed) and/or rely on LIMIT as a cost-control mechanism. In BigQuery, projecting excess columns increases the amount of data read and can materially raise query cost, particularly on wide... Databases - GCP BigQuery CER-0226 ### [Unoptimized Billing Model for BigQuery Dataset Storage](https://www.pointfive.co/efficiency-hub/inefficiencies/unoptimized-billing-model-for-bigquery-dataset-storage) Highly compressible datasets, such as those with repeated string fields, nested structures, or uniform rows, can benefit significantly from physical storage billing. Yet most datasets remain on logical storage by default, even when... Databases - GCP BigQuery CER-0024 ### [Unnecessary Reset of Long-Term Storage Pricing in BigQuery](https://www.pointfive.co/efficiency-hub/inefficiencies/unnecessary-reset-of-long-term-storage-pricing-in-bigquery) BigQuery incentivizes efficient data retention by cutting storage costs in half for tables or partitions that go 90 days without modification. However, many teams unintentionally forfeit this discount by performing broad or unnecessary... Databases --- Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.