GCP Billing Export to BigQuery: Complete Setup Guide (2026)

Billing export to BigQuery is the foundation of GCP cost optimization. Without it, you're limited to summary views in the Console. With it, you can analyze every dollar spent, build custom dashboards, and catch cost anomalies.

Critical: Billing export has limited retroactive data. If you use a multi-region dataset (US or EU), you get data for the current and previous month. Regional datasets only get data from the day you enable export. Either way, every month you wait is historical data lost forever.

This guide walks you through setup, explains the export types, and gives you queries to start analyzing immediately.

Why Enable Billing Export?

Before we start, here's why this matters:

Step 1: Create a BigQuery Dataset

First, create a dataset to store your billing data.

In the Console

1. Go to BigQuery in the Console
2. In the Explorer panel, click your project
3. Click Create dataset
4. Configure:
   • Dataset ID: billing_export (or any name you prefer)
   • Data location: Select US or EU multi-region (recommended by Google)
   • Default table expiration: Leave blank (you want to keep this data)
   • Encryption: Google-managed key (default)
5. Click Create dataset

Recommendation: Google recommends selecting a multi-region location (US or EU). Multi-region datasets get a retroactive backfill of the current and previous month's data. Regional datasets only capture data from the day you enable export—no backfill.

Step 2: Enable Billing Export

Navigate to Billing Export

1. Go to Billing in the Console
2. Select your billing account
3. Click Billing export in the left menu

Enable Each Export Type

Google offers four export types. Enable the ones you need:

1. Standard Usage Cost (Recommended)

What it includes: Daily cost data at the SKU level
Table name: gcp_billing_export_v1_<BILLING_ACCOUNT_ID>
Best for: General cost analysis, trend tracking

To enable:

1. Click Edit settings under "Standard usage cost"
2. Select your project and dataset
3. Click Save

2. Detailed Usage Cost (Recommended)

What it includes: Resource-level detail (individual VMs, disks, etc.)
Table name: gcp_billing_export_resource_v1_<BILLING_ACCOUNT_ID>
Best for: Finding specific expensive resources, per-resource analysis

To enable:

1. Click Edit settings under "Detailed usage cost"
2. Select your project and dataset
3. Click Save

3. Pricing Export

What it includes: SKU prices for your billing account
Table name: cloud_pricing_export
Best for: Price comparison, forecasting, rate card analysis

To enable:

1. Click Edit settings under "Pricing"
2. Select your project and dataset
3. Click Save

4. CUD Metadata (If you use CUDs)

What it includes: Commitment details, utilization data
Best for: Monitoring CUD usage

Step 3: Wait for Data

After enabling export, data doesn't appear immediately.

Timeline:

• Initial data: 24-48 hours
• Backfill (if applicable): Up to 5 days for recent data
• Ongoing: Updates throughout the day

SELECT COUNT(*) as row_count, MIN(export_time) as earliest, MAX(export_time) as latest
FROM your-project.billing_export.gcp_billing_export_v1_XXXXXX

If you get results, export is working.

Understanding the Schema

Key Columns in Standard Export

Key Columns in Detailed Export (Additional)

Your First Queries

Once data is flowing, try these queries to start analyzing.

Query 1: Total Cost by Service (This Month)

SELECT
  service.description AS service,
  ROUND(SUM(cost), 2) AS total_cost,
  ROUND(SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)), 2) AS credits,
  ROUND(SUM(cost) + SUM(IFNULL((SELECT SUM(c.amount) FROM UNNEST(credits) c), 0)), 2) AS net_cost
FROM your-project.billing_export.gcp_billing_export_v1_XXXXXX
WHERE invoice.month = FORMAT_DATE('%Y%m', CURRENT_DATE())
GROUP BY service
ORDER BY net_cost DESC

Query 2: Daily Cost Trend (Last 30 Days)

SELECT
  DATE(usage_start_time) AS date,
  ROUND(SUM(cost), 2) AS daily_cost
FROM your-project.billing_export.gcp_billing_export_v1_XXXXXX
WHERE usage_start_time >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
GROUP BY date
ORDER BY date

Query 3: Cost by Project

SELECT
  project.id AS project,
  ROUND(SUM(cost), 2) AS total_cost
FROM your-project.billing_export.gcp_billing_export_v1_XXXXXX
WHERE invoice.month = FORMAT_DATE('%Y%m', CURRENT_DATE())
GROUP BY project
ORDER BY total_cost DESC

Query 4: Top 10 Most Expensive SKUs

SELECT
  service.description AS service,
  sku.description AS sku,
  ROUND(SUM(cost), 2) AS total_cost
FROM your-project.billing_export.gcp_billing_export_v1_XXXXXX
WHERE invoice.month = FORMAT_DATE('%Y%m', CURRENT_DATE())
GROUP BY service, sku
ORDER BY total_cost DESC
LIMIT 10

Query 5: Cost by Label (if you use labels)

SELECT
  (SELECT value FROM UNNEST(labels) WHERE key = 'environment') AS environment,
  ROUND(SUM(cost), 2) AS total_cost
FROM your-project.billing_export.gcp_billing_export_v1_XXXXXX
WHERE invoice.month = FORMAT_DATE('%Y%m', CURRENT_DATE())
GROUP BY environment
ORDER BY total_cost DESC

Query 6: Find Specific Resource Costs (Detailed Export)

SELECT
  resource.name AS resource_name,
  service.description AS service,
  ROUND(SUM(cost), 2) AS total_cost
FROM your-project.billing_export.gcp_billing_export_resource_v1_XXXXXX
WHERE invoice.month = FORMAT_DATE('%Y%m', CURRENT_DATE())
  AND resource.name IS NOT NULL
GROUP BY resource_name, service
ORDER BY total_cost DESC
LIMIT 20

Building a Simple Dashboard

Once you have data, you can build dashboards in Looker Studio (formerly Data Studio).

Quick Dashboard Setup

1. Go to Looker Studio
2. Click Create → Data source
3. Select BigQuery
4. Choose your billing export table
5. Click Connect
6. Create a new report with these charts:
   • Scorecard: Total monthly cost
   • Time series: Daily cost trend
   • Pie chart: Cost by service
   • Table: Cost by project

Pre-Built Dashboard Templates

Google provides templates at the Cloud Billing documentation.

Common Issues and Troubleshooting

"No data in my table"

Causes:

• Export just enabled (wait 24-48 hours)
• Wrong dataset selected
• Billing account has no spend

"Data seems incomplete"

Causes:

• Initial backfill still processing
• Querying wrong date range

"I need historical data"

Good news (limited): If your dataset uses a multi-region location (US or EU), you get retroactive data for the current and previous month. This backfill happens automatically but can take up to 5 days.

Bad news: You cannot recover data older than one month before enabling export. Regional datasets get no backfill at all.

Partial solution: For older history, you can use the Billing Reports export (CSV) for historical summaries, but it won't have the line-item detail of BigQuery export.

"Table names are confusing"

Tables follow this pattern:

• Standard: gcp_billing_export_v1_<BILLING_ACCOUNT_ID>
• Detailed: gcp_billing_export_resource_v1_<BILLING_ACCOUNT_ID>

Example: If your billing account is 01A2B3-C4D5E6-F7G8H9, your table is:
gcp_billing_export_v1_01A2B3_C4D5E6_F7G8H9

"BigQuery costs are adding up"

Causes:

• Scanning large tables repeatedly
• No partitioning in queries

WHERE usage_start_time >= '2025-01-01'

Best Practices

1. Enable Both Standard and Detailed Exports

Standard gives you SKU-level analysis. Detailed gives you resource-level. You'll want both eventually.

2. Use Scheduled Queries for Regular Reports

Instead of running queries manually, schedule them:

1. Write your query in BigQuery
2. Click Schedule → Create new scheduled query
3. Set frequency and destination table

3. Set Up Cost Alerts

Use BigQuery scheduled queries + Cloud Monitoring to alert on:

• Daily spend exceeds threshold
• Week-over-week increase above X%
• Specific service costs spike

4. Document Your Table Names

Billing account IDs in table names are hard to remember. Create a view with a friendly name:

CREATE VIEW your-project.billing_export.billing_standard AS
SELECT * FROM your-project.billing_export.gcp_billing_export_v1_01A2B3_C4D5E6_F7G8H9

5. Consider Data Retention

Billing export tables grow over time. Decide how much history you need and consider:

• Archiving old data to cheaper storage
• Creating summary tables for long-term trends

6. Prepare for Schema Changes (CUD Users)

If you use spend-based Committed Use Discounts (CUDs), be aware that Google is updating the billing export schema on January 21, 2026. Key changes:

• CUD discounts move from credits to a new consumption_model field
• New fields: price.list_price, price.effective_price_default, consumption_model struct
• Two rows per CUD SKU instead of one (separating overage from discounted usage)

What's Next After Setup?

Now that you have billing data in BigQuery, you can:

1. Build custom dashboards for your team
2. Set up anomaly alerts for unexpected cost spikes
3. Analyze optimization opportunities like idle resources
4. Track CUD utilization if using committed discounts
5. Allocate costs by team/project using labels

Key Takeaways

1. Enable billing export today—data is not retroactive
2. Enable both standard and detailed exports
3. Wait 24-48 hours for initial data, up to 5 days for backfill
4. Use date filters in queries to control BigQuery costs
5. Build on this foundation with dashboards, alerts, or third-party tools

Want to skip the dashboard building? GCP FinOps by Brain Agents AI connects to your billing data and provides ready-to-use cost insights and recommendations.

Related Articles:

• GCP Cost Optimization Guide for Growing Companies
• Understanding GCP Committed Use Discounts: Should Your Company Buy CUDs?
• Best GCP Cost Optimization Tools for Growing Companies