# Unoptimized billing model for BigQuery dataset storage Canonical: 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... By: Laurent Dumont Updated: 2026-09-28 [Cloud Efficiency Hub](https://www.pointfive.co/efficiency-hub) # Unoptimized Billing Model for BigQuery Dataset Storage The short version Highly compressible datasets, such as those with repeated string fields, nested structures, or uniform rows, can benefit significantly from physical storage billing. [Laurent Dumont](https://www.linkedin.com/in/laurentfdumont/) Senior DevOps @ Desjardins 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-0226 Type Inefficient Configuration ## Explanation Why the waste happens and who it affects. Yet most datasets remain on logical storage by default, even when physical storage would reduce costs. This inefficiency is common for cold or infrequently updated datasets that are no longer optimized or regularly reviewed. Because storage behavior and data characteristics evolve, failing to periodically reassess the billing model may result in persistent waste. ## Billing model The pricing dimensions that drive this cost. BigQuery supports two billing models for table storage: Logical Storage (default): Billed based on the uncompressed size of user data. This model includes time travel and fail-safe storage at no extra cost. Physical Storage: Billed based on the actual compressed bytes on disk. Time travel and fail-safe storage are charged separately at the same rate as active storage. While physical storage can significantly reduce costs for highly compressible datasets, its per-GiB rates are higher than the logical rates (the gap varies by region and differs between active and long-term storage), so it only pays off when the logical-to-physical compression ratio exceeds that price ratio after adding time travel and fail-safe bytes. For frequently updated or less compressible data, the higher rate and the separate retention charges may lead to higher total costs. ## How to detect 7 checks to find it in your estate. - Evaluate datasets with high logical-to-physical compression ratios. - The billing model is applied at the dataset level. Make sure that the compression ratio is aligned to all tables within the dataset. - Run periodic queries against \`INFORMATION\_SCHEMA.TABLE\_STORAGE\` to compare logical and physical storage sizes across multiple dimensions: active, long-term, time travel and fail-safe. - Prioritize datasets that are infrequently updated (therefore with low time travel and fail-safe storage volumes) or primarily used for historical lookback - Use the storage pricing to simulate cost in the physical model \\- multiply by the new rates, and add the time travel and fail-safe storage bytes. - Flag datasets with simulated physical storage price lower than the current logical storage price - Balance the frequency of detection queries with their cost, and avoid excessive scanning of large datasets ## How to fix 10 ways to remove the waste. - Switch eligible datasets to physical storage billing when compression advantages are material - There is no performance impact between the two billing models. - Changing the billing model takes 24 hours before it's reflected in the GCP billing SKUs. - There is a 14-day waiting period when a change is made to the billing model. - Periodically reassess dataset compression ratios to determine if billing model changes are warranted - Use partitioning and clustering to improve compressibility where possible - Apply billing model changes to cold or infrequently modified data first, as their structure is more stable - Document the dataset billing model decisions to ensure transparency and reproducibility - Monitor billing changes for 2-3 months after switching to validate expected savings - Consider dataset lifecycle - BigQuery has no storage classes, but tables and partitions left unmodified for 90 consecutive days are billed at the long-term rate automatically; for data that is no longer queried, set partition expiration or export it to Cloud Storage (for example, the Coldline or Archive class) before deleting it from BigQuery ## Documentation Vendor references for pricing and configuration. - [Physical vs logical storage cloud.google.com](https://cloud.google.com/bigquery/docs/datasets-intro#dataset_storage_billing_models) - [Querying INFORMATION\\\_SCHEMA for table metadata cloud.google.com](https://cloud.google.com/bigquery/docs/information-schema-tables) - [TABLE\\\_STORAGE view cloud.google.com](https://cloud.google.com/bigquery/docs/information-schema-table-storage#calculating_storage_billing) - [BigQuery Pricing cloud.google.com](https://cloud.google.com/bigquery/pricing?hl=en#section-4) - [BigQuery storage forecast queries cloud.google.com](https://cloud.google.com/bigquery/docs/information-schema-table-storage) ## Related inefficiencies [Browse the library](https://www.pointfive.co/efficiency-hub) - GCP BigQuery CER-0484 ### [Missing Partitioning and Clustering on Large BigQuery Tables](https://www.pointfive.co/efficiency-hub/inefficiencies/missing-partitioning-and-clustering-on-large-bigquery-tables) BigQuery charges for queries by the data they process (on-demand pricing) or by the slot capacity they consume (capacity pricing). When a large table is neither partitioned nor clustered, a query that filters on a date or key column still... Databases - 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-0069 ### [Inefficient Use of Reservations in BigQuery](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 for heavy, recurring workloads. However, if query volumes drop - due to seasonal cycles,... Databases --- Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.