GCP Cloud Billing Export to Bucket is deprecated and now GCP supports billing through BigQuery.

Cloud Billing export to BigQuery enables you to export detailed Google Cloud billing data (such as usage, cost estimates, and pricing data) automatically throughout the day to a BigQuery dataset that you specify.

The setup guide provides best practice recommendations and detailed instructions for enabling Cloud Billing data export to BigQuery.

To export Cloud Billing data to BigQuery, take the following steps:

  • Create a project where the Cloud Billing data will be stored, and enable billing on the project (if you have not already done so).
  • Configure permissions on the project and on the Cloud Billing account.
  • Enable the BigQuery Data Transfer Service API (required to export your pricing data).
  • Create a BigQuery dataset in which to store the data.
  • Enable Cloud Billing export of cost data and pricing data to be written into the dataset.


Each bill has a billing day of the month which is typically calculated as below:

  • The start time of a particular month’s billing is a few hours past the 1st of every month and the end time is considered a few hours past the 1st of the next month.

For example, start and end time for the month of April can be anywhere between 2022-04-01T00:00:00 to 2022-04-01T08:00:00 hrs and the end time could be anywhere from 2022-05-01T00:00:00 to 2022-05-01T08:00:00, ideally, the start and end time for the month of April should be 2022-04-01T00:00:00 to 2022-04-30T23:59:59.

Below images show how to enable billing export for the created dataset in Google Cloud Platform.


Once you enabled the billing export for the created dataset in Google Cloud Platform, perform below steps:

  1. Login to OpsRamp account with your credentials.
  2. Select a client.
  3. Go to Setup > Integration and Apps > Integrations.
  4. Select installed Google Cloud integration.
  5. In the Discovery Profiles, Edit of a profile for which you want to add the dataset name.
  6. Under Perform Actions, paste the dataset name in Dataset Name field as shown below: