Pricing

BigQuery offers scalable, flexible pricing options to help fit your project and your budget.

BigQuery storage costs are based solely on the amount of data you store. Storage charges can be:

  • Active — A monthly charge for data stored in tables you have modified in the last 90 days.
  • Long-term — A lower monthly charge for data stored in tables that have not been modified in the last 90 days.

Query costs are based on the amount of data processed by the query. Query charges can be:

  • On-demand — The most flexibile option. On-demand query pricing is based solely on usage.
  • Flat-rate — Enterprise customers generally prefer flat-rate pricing for queries because it offers predictable, fixed month-to-month costs.

For more information on storage and query pricing, see Google Cloud Platform SKUs.

Pricing summary

The following table summarizes BigQuery pricing. BigQuery's Quotas and Limits apply to these operations.

US (multi-region) EU (multi-region) Tokyo
Monthly
Operation Pricing Details
Active storage The first 10 GB is free each month. See Storage pricing for details.
Long-term storage The first 10 GB is free each month. See Storage pricing for details.
Streaming Inserts You are charged for rows that are successfully inserted. Individual rows are calculated using a 1 KB minimum size. See Streaming pricing for details.
Queries (analysis) First 1 TB per month is free, see On-demand pricing for details. Flat-rate pricing is also available for high-volume customers.

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

How charges are billed

Each project you create has a billing account attached to it. Any charges incurred by BigQuery jobs run in the project (such as query jobs) are billed to the attached billing account. This is true even if project resources are shared with others outside your organization. BigQuery storage costs are also billed to the attached billing account.

How to analyze billing data

You can view BigQuery costs and trends by using the Cloud Billing Reports page in the Cloud Console. For information on analyzing billing data using reports, see View Your Cost Trends With Billing Reports.

For information on analyzing your billing data in BigQuery, see Export Billing Data to BigQuery in the Cloud Billing documentation.

Free operations

The following table shows BigQuery operations that are free of charge in every location. BigQuery's Quotas and Limits apply to these operations.

Operation Details
Loading data

When you load data into BigQuery from Cloud Storage, you are not charged for the load operation, but you do incur charges for storing the data in Cloud Storage. See Data storage on the Cloud Storage pricing page for details. Once the data is loaded into BigQuery, the data is subject to BigQuery's Storage pricing. For more information, see Loading Data into BigQuery.

When you create a dataset in BigQuery, you must choose a location for the data. If you choose US, you can load data into tables in the dataset from a Cloud Storage bucket in any other region. When you load data from another region into a US dataset, there is currently no charge for internet egress.

If you choose a location other than US, you must do one of the following:

  • Load data from a Cloud Storage bucket in that region (the bucket can be either a multi-regional bucket or a regional bucket in the same region as the dataset)
  • Copy the data into a bucket in that region

When you copy data from one Cloud Storage region to another, Cloud Storage network pricing applies.

Copying data You are not charged for copying a table, but you do incur charges for storing the new table and the table you copied. For more information, see Copying an existing table.
Exporting data When you export data from BigQuery to Cloud Storage, you are not charged for the export operation, but you do incur charges for storing the data in Cloud Storage. See Data storage on the Cloud Storage pricing page for details. For more information, see Exporting Data from BigQuery.
Deleting datasets You are not charged for deleting a dataset.
Deleting tables, views, and partitions You are not charged for deleting a table, deleting a view, or deleting individual table partitions.
Metadata operations You are not charged for list, get, patch, update and delete calls. Examples include (but are not limited to): listing datasets, updating a dataset's access control list, and updating a table's description.

Always free usage limits

As part of the Google Cloud Platform Free Tier, BigQuery offers some resources free of charge, up to a specific limit. These free usage limits are available during and after the free trial period. If you go over these usage limits and are no longer in the free trial period, you will be charged according to the pricing on this page.

The following BigQuery resources are free of charge:

  • The first 10 GB of storage each month (per billing account)
  • The first 1 TB of query data processed each month (per billing account)

Query pricing

Query pricing refers to the cost of running your SQL commands and user-defined functions. BigQuery charges for queries by using one metric: the number of bytes processed (also referred to as bytes read). You are charged for the number of bytes processed whether the data is stored in BigQuery or in an external data source such as Cloud Storage, Google Drive, or Cloud Bigtable.

When you run a query, you're charged according to the total data processed in the columns you select, even if you set an explicit LIMIT on the results. The total bytes per column is calculated based on the types of data in the column. For more information about how we calculate your data size, see Data size calculation.

Query pricing is based on your usage pattern: a monthly flat rate for queries or pricing based on interactive queries. Enterprise customers generally prefer flat-rate pricing for queries because that model offers consistent month-to-month costs. On-demand (or interactive) pricing offers flexibility and is based solely on usage.

On-demand pricing

On-demand query pricing is as follows:

US (multi-region) EU (multi-region) Tokyo
Monthly
Operation Pricing Details
Queries (analysis) First 1 TB per month is free. Flat-rate pricing is also available for high-volume customers that prefer a stable, monthly cost.

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Note the following regarding query charges:

  • You aren't charged for queries that return an error, or for queries that retrieve results from the cache.
  • Charges are rounded to the nearest MB, with a minimum 10 MB data processed per table referenced by the query, and with a minimum 10 MB data processed per query.
  • Cancelling a running query job may incur charges up to the full cost for the query were it allowed to run to completion.
  • BigQuery uses a columnar data structure. You're charged according to the total data processed in the columns you select, and the total data per column is calculated based on the types of data in the column. For more information about how your data size is calculated, see data size calculation.

On-demand query cost controls

BigQuery provides cost control mechanisms that enable you to cap your query costs. You can set:

Flat-rate pricing

BigQuery offers flat-rate pricing for high-volume or enterprise customers who prefer a stable monthly cost for queries rather than paying the on-demand price per TB of data processed. If you choose flat-rate pricing, the cost of all bytes processed is included in the monthly flat-rate price.

BigQuery automatically manages your slots quota based on customer history, usage, and spend. For customers with at least $40,000 in monthly analytics spend BigQuery offers several ways to increase the number of allocated slots.

Flat-rate pricing:

  • Applies only to query costs, not for storage. See Storage pricing for storage costs.
  • Applies to all projects that are linked to the billing account where flat-rate pricing is applied.
  • Provides additional BigQuery slots. See the following table for details.
  • Provides additional query concurrency for interactive queries.

US (multi-region) EU (multi-region) Tokyo
Monthly
Monthly cost Allocated slots Details
2,000 For each $10,000 increment above the base rate, you are allocated 500 additional slots.

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Contact your sales representative if you are interested in flat-rate pricing.

Storage pricing

Once your data is loaded into BigQuery, you are charged for storing it. Storage pricing is based on the amount of data stored in your tables when it is uncompressed.

The size of the data is calculated based on the data types of the individual columns. For a detailed explanation of how data size is calculated, see Data size calculation.

Active storage

Active storage charges are as follows:

US (multi-region) EU (multi-region) Tokyo
Monthly
Storage type Pricing Details
Active storage The first 10 GB is free each month.

If you pay in a currency other than USD, the prices listed in your currency on Cloud Platform SKUs apply.

Storage pricing is prorated per MB, per second. For example, if you store:

  • 100 MB for half a month, you pay $0.001 (a tenth of a cent)
  • 500 GB for half a month, you pay $5
  • 1 TB for a full month, you pay $20

Long-term storage

If a table is not edited for 90 consecutive days, the price of storage for that table automatically drops by approximately 50 percent. There is no degradation of performance, durability, availability, or any other functionality when a table is considered long term storage.

Long-term storage pricing is as follows:

US (multi-region) EU (multi-region) Tokyo
Monthly
Storage type Pricing Details
Long-term storage The first 10 GB is free each month.

If the table is edited, the price reverts back to the regular storage pricing, and the 90-day timer starts counting from zero. Anything that modifies the data in a table resets the timer, including:

Action Details
Loading data into a table Any load or query job that appends data to a destination table or overwrites a destination table.
Copying data into a table Any copy job appends data to a destination table or overwrites a destination table.
Writing query results to a table Any query job that appends data to a destination table or overwrites a destination table.
Using the Data Manipulation Language (DML) Using a DML statement to modify table data.
Streaming data into the table Ingesting data using the tabledata.insertAll API call.

All other actions do not reset the timer, including:

  • Querying a table
  • Creating a view that queries a table
  • Exporting data from a table
  • Copying a table (to another destination table)
  • Patching or updating a table resource

Each partition of a partitioned table is considered separately for long-term storage pricing. If a partition hasn't been modified in the last 90 days, the data in that partition is considered long term storage and is charged at the discounted price.

For tables that reach the 90-day threshold during a billing cycle, the price is prorated accordingly.

Long term storage pricing applies only to BigQuery storage, not to data stored in external data sources such as Cloud Bigtable, Cloud Storage, and Google Drive.

Data size calculation

When you load data into BigQuery or query the data, you're charged according to the data size. Data size is calculated based on the size of each column's data type.

The size of your stored data, and the size of the data processed by your queries is calculated in gigabytes (GB), where 1GB is 230 bytes. This unit of measurement is also known as a gibibyte (GiB). Similarly, 1TB is 240 bytes (1024 GBs).

The size of BigQuery's data types is as follows:

Data type Size
INT64/INTEGER 8 bytes
FLOAT64/FLOAT 8 bytes
NUMERIC 16 bytes
BOOL/BOOLEAN 1 byte
STRING 2 bytes + the UTF-8 encoded string size
BYTES 2 bytes + the UTF-8 encoded string size
DATE 8 bytes
DATETIME 8 bytes
TIME 8 bytes
TIMESTAMP 8 bytes
STRUCT/RECORD 0 bytes + the size of the contained fields

Null values for any data type are calculated as 0 bytes.

A repeated column is stored as an array, and the size is calculated based on the number of values. For example, an integer column (INT64) that is repeated (ARRAY<INT64>) and contains 4 entries is calculated as 32 bytes (4 entries x 8 bytes).

Streaming pricing

Loading data into BigQuery is free, with the exception of a small charge for streamed data.

Pricing for streaming inserts is as follows:

US (multi-region) EU (multi-region) Tokyo
Monthly
Operation Pricing Details
Streaming Inserts You are charged for rows that are successfully inserted. Individual rows are calculated using a 1 KB minimum size.

Data Manipulation Language pricing

BigQuery charges for DML queries based on the number of bytes processed by the query.

DML pricing for non-partitioned tables

For non-partitioned tables, the number of bytes processed is calculated as follows:

DML statement Bytes processed
INSERT The sum of bytes processed for all the columns referenced from the tables scanned by the query.
UPDATE The sum of bytes in all the columns referenced from the tables scanned by the query
+ the sum of bytes for all columns in the updated table at the time the UPDATE starts.
DELETE The sum of bytes in all the columns referenced from the tables scanned by the query
+ the sum of bytes for all columns in the modified table at the time the DELETE starts.
MERGE If there are only INSERT clauses in the MERGE statement, you are charged for the sum of bytes processed for all the columns referenced in all tables scanned by the query.
If there is an UPDATE or DELETE clause in the MERGE statement, you are charged for the sum of the bytes processed for all the columns referenced in the source tables scaned by the query
+ the sum of bytes for all columns in the target table (at the time the MERGE starts).

DML pricing for partitioned tables

For partitioned tables, the number of bytes processed is calculated as follows:

DML statement Bytes processed
INSERT The sum of bytes processed for all the columns referenced in all partitions scanned by the query.
UPDATE The sum of bytes processed for all the columns referenced in all partitions for the tables scanned by the query
+ the sum of bytes for all columns in the updated or scanned partitions for the table being updated (at the time the UPDATE starts).
DELETE The sum of bytes processed for all the columns referenced in all partitions for the tables scanned by the query
+ the sum of bytes for all columns in the modified or scanned partitions for the table being modified (at the time the DELETE starts).
MERGE If there are only INSERT clauses in the MERGE statement, you are charged for the sum of bytes processed for all the columns referenced in all partitions scanned by the query.
If there is an UPDATE or DELETE clause in the MERGE statement, you are charged for the sum of the bytes processed for all the columns referenced in all partitions for the source tables scaned by the query
+ the sum of bytes for all columns in the updated, deleted or scanned partitions for the target table (at the time the MERGE starts).

BigQuery Data Transfer Service pricing

The BigQuery Data Transfer Service charges monthly on a prorated basis. You are charged as follows:

Source application Monthly charge (prorated)
Google AdWords

$2.50 per unique Customer ID — ExternalCustomerIDs in the Customer table, including zero impression Customer IDs.

DoubleClick Campaign Manager

$2.50 per unique Advertiser ID — Advertiser IDs in the impression table.

DoubleClick for Publishers

$100 per Network ID

YouTube Channel and YouTube Content Owner

No charge through July 1, 2018. Pricing for YouTube to be announced at a later date.

After data is transferred to BigQuery, standard BigQuery storage and query pricing applies. For additional pricing details, contact Sales.

Calculating unique IDs

Each transfer you create generates 1 or more runs per day. Each run maintains a record of each unique ID encountered and the date the transfer run completes. IDs are only counted on the day the transfer completes. For example, if a transfer run begins on July 14th but completes on July 15th, the unique IDs are counted on July 15th.

If a unique ID is encountered in more than one transfer run on a particular day, it is counted only once. Unique IDs are counted separately for different transfers. If a unique ID is encountered in runs for two separate transfers, the ID is counted twice.

Calculating backfill charges

If you schedule a backfill, a transfer run is scheduled for each day. You are then charged based on the method described in Calculating unique IDs.

Stopping BigQuery Data Transfer Service charges

To stop incurring charges, disable or delete your transfer.

Pricing examples

Estimating query costs

For query pricing examples, see Estimating query costs.

Estimating storage costs

For storage pricing examples, see Estimating storage costs.

DML pricing examples for non-partitioned tables

The following examples demonstrate how BigQuery calculates bytes read for DML statements that modify non-partitioned tables.

Example 1: Non-partitioned table UPDATE

table1 has two columns: col1 of type INTEGER and col2 of type STRING.

UPDATE table1 SET col1 = 1 WHERE col1 = 2;

Bytes processed in this example =

  • sum of the number of bytes in col1 +
  • sum of the number of bytes in col2

Example 2: Non-partitioned table UPDATE

table1 has two columns: col1 of type INTEGER and col2 of type STRING. table2 has one column: field1 of type INTEGER.

UPDATE table1 SET col1 = 1 WHERE col1 in (SELECT field1 from table2)

Bytes processed in this example =

  • sum of the number of bytes in table1.col1 before UPDATE +
  • sum of the number of bytes in table1.col2 before UPDATE +
  • sum of the number of bytes in table2.field1

DML pricing examples for partitioned tables

The following examples demonstrate how BigQuery calculates bytes read for DML statements that modify ingestion-time and partitioned tables. To view the JSON schema representations for the tables used in the examples, see Tables used in examples on the Updating Partitioned Table Data Using DML Statements page.

Example 1: Ingestion-time partitioned table INSERT

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mytable has two columns: field1 of type INTEGER and field2 of type STRING.

INSERT INTO mytable (_PARTITIONTIME, field1) AS SELECT TIMESTAMP(DATE(ts)), id from mytable2

Bytes processed in this example =

  • sum of the number of bytes in mytable2.ts +
  • sum of the number of bytes in mytable2.id

The size of table into which the rows are inserted — mytable — does not affect the cost of the query.

Example 2: Partitioned table INSERT

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mycolumntable has four columns: field1 of type INTEGER, field2 of type STRING, field3 of type BOOLEAN, and ts of type TIMESTAMP.

INSERT INTO mycolumntable (ts, field1) AS SELECT ts, id from mytable2

Bytes processed in this example =

  • sum of the number of bytes in mytable2.ts +
  • sum of the number of bytes in mytable2.id

The size of table into which the rows are inserted — mycolumntable — does not affect the cost of the query.

Example 3: Ingestion-time partitioned table UPDATE

DML statement 1: Updating a single partition

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mytable has two columns: field1 of type INTEGER and field2 of type STRING.

UPDATE project.mydataset.mytable T SET T.field1 = T.field1 + 100 WHERE T._PARTITIONTIME = TIMESTAMP(“2017-05-01”) AND EXISTS (SELECT S.id from project.mydataset.mytable2 S WHERE S.id = T.field1)

Bytes processed in this example =

  • sum of the number of bytes in mytable2.id +
  • sum of the number of bytes in mytable.field1 in the "2017-05-01" partition +
  • sum of the number of bytes in mytable.field2 in the "2017-05-01" partition

DML statement 2: Updating a partition based on another partition in the table

UPDATE project.mydataset.mytable T SET T._PARTITIONTIME = TIMESTAMP(“2017-06-01”), T.field1 = T.field1 + 100 WHERE T._PARTITIONTIME = TIMESTAMP(“2017-05-01”) AND EXISTS (SELECT 1 from project.mydataset.mytable S WHERE S.field1 = T.field1 AND S._PARTITIONTIME = TIMESTAMP("2017-06-01") )

Bytes processed in this example =

  • sum of the number of bytes in mytable.field1 in the "2017-05-01" partition +
  • sum of the number of bytes in mytable.field2 in the "2017-05-01" partition +
  • sum of the number of bytes in mytable.field1 in the "2017-06-01" partition +
  • sum of the number of bytes in mytable.field2 in the "2017-06-01" partition

In this case, the cost of the UPDATE statement is the sum of sizes of all fields in the partitions corresponding to "2017-05-01" and "2017-06-01".

Example 4: Partitioned table UPDATE

DML statement 1: Updating a single partition

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mycolumntable has four columns: field1 of type INTEGER, field2 of type STRING, field3 of type BOOLEAN, and ts of type TIMESTAMP.

UPDATE project.mydataset.mycolumntable T SET T.field1 = T.field1 + 100 WHERE DATE(T.ts) = “2017-05-01” AND EXISTS (SELECT S.id from project.mydataset.mytable2 S WHERE S.id = T.field1)

Bytes processed in this example =

  • sum of the number of bytes in mytable2.id +
  • sum of the number of bytes in mycolumntable.field1 in the "2017-05-01" partition +
  • sum of the number of bytes in mycolumntable.field2 in the "2017-05-01" partition +
  • sum of the number of bytes in mycolumntable.field3 in the "2017-05-01" partition +
  • sum of the number of bytes in mycolumntable.ts in the "2017-05-01" partition

DML statement 2: Updating a partition based on another partition in the table

UPDATE project.mydataset.mycolumntable T SET T.ts = TIMESTAMP(“2017-06-01”), T.field1 = T.field1 + 100 WHERE DATE(T.ts) = “2017-05-01” AND EXISTS (SELECT 1 from project.mydataset.mycolumntable S WHERE S.field1 = T.field1 AND DATE(S.ts) = "2017-06-01")

Bytes processed in this example =

  • sum of the number of bytes in mycolumntable.field1 in the "2017-05-01" partition +
  • sum of the number of bytes in mycolumntable.field2 in the "2017-05-01" partition +
  • sum of the number of bytes in mycolumntable.field3 in the "2017-05-01" partition +
  • sum of the number of bytes in mycolumntable.ts in the "2017-05-01" partition +
  • sum of the number of bytes in mycolumntable.field1 in the "2017-06-01" partition +
  • sum of the number of bytes in mycolumntable.field2 in the "2017-06-01" partition +
  • sum of the number of bytes in mycolumntable.field3 in the "2017-06-01" partition +
  • sum of the number of bytes in mycolumntable.ts in the "2017-06-01" partition

In this case, the cost of the UPDATE statement is the sum of sizes of all fields in the partitions corresponding to "2017-05-01" and "2017-06-01".

Example 5: Ingestion-time partitioned table DELETE

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mytable has two columns: field1 of type INTEGER and field2 of type STRING.

DELETE project.mydataset.mytable T WHERE T._PARTITIONTIME = TIMESTAMP(“2017-05-01”) AND EXISTS (SELECT S.id from project.mydataset.mytable2 S WHERE S.id = T.field1)

Bytes processed in this example =

  • sum of the number of bytes in mytable2.id +
  • sum of the number of bytes in mytable.field1 in the "2017-05-01" partition +
  • sum of the number of bytes in mytable.field2 in the "2017-05-01" partition

Example 6: Partitioned table DELETE

mytable2 has two columns: id of type INTEGER and ts of type TIMESTAMP. mycolumntable has four columns: field1 of type INTEGER, field2 of type STRING, field3 of type BOOLEAN, and ts of type TIMESTAMP.

DELETE project.mydataset.mycolumntable T WHERE DATE(T.ts) =“2017-05-01” AND EXISTS (SELECT S.id from project.mydataset.mytable2 S WHERE S.id = T.field1)

Bytes processed in this example =

  • sum of the number of bytes in mytable2.id +
  • sum of the number of bytes in mycolumntable.field1 in the "2017-05-01" partition +
  • sum of the number of bytes in mycolumntable.field2 in the "2017-05-01" partition +
  • sum of the number of bytes in mycolumntable.field3 in the "2017-05-01" partition +
  • sum of the number of bytes in mycolumntable.ts in the "2017-05-01" partition

BigQuery Data Transfer Service pricing examples

Example 1: You have 1 transfer with 3 runs that complete on the same day.

  • The first run records the following unique IDs: A, B, and C
  • The second run records: A
  • The third run records: C and D

Since all runs finish on the same day, you are charged based on 4 unique IDs — A, B, C, D. Because ID A and ID C were recorded in two different runs that completed on the same day, IDs A and C are counted only once. If the 3 transfer runs complete every day for a month, your monthly charge is based on 4 unique IDs. If the transfer runs complete fewer times than the number of days in the month in which they run, the charges are prorated.

Example 2: You have multiple transfers with runs that complete on the same day.

  • Transfer 1 runs and records the following unique IDs: A, B, and C
  • Transfer 2 runs and records: A
  • Transfer 3 runs and records: C and D

Because the unique IDs are counted in runs for different transfers, you are charged based on 6 unique IDs — A, B, and C from transfer 1's run; A from transfer 2's run; and C and D from transfer 3's run. If the transfer runs complete fewer times than the number of days in the month in which they run, the charges are prorated.

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