Estimating and Forecasting GCP Spend Using BigQuery ML
1. Introduction
Managing cloud costs is a critical challenge for organizations using Google Cloud Platform (GCP). As usage grows across services like Compute Engine, BigQuery, Cloud Storage, and Kubernetes, predicting future spend becomes essential for:
Budget planning
Cost optimization
Preventing billing surprises
Capacity and growth forecasting
BigQuery ML (BQML) allows you to build and deploy machine learning models directly in SQL, making it an ideal tool for forecasting GCP spend without exporting data to external ML platforms.
2. Architecture Overview
The typical workflow looks like this:
Collect billing data from GCP Billing Export
Store and transform cost data in BigQuery
Train a forecasting model using BigQuery ML
Generate future spend predictions
Visualize and monitor results using Looker or dashboards
3. Data Source: GCP Billing Export
3.1 Enable Billing Export to BigQuery
GCP provides a Billing Export feature that automatically exports detailed cost data into BigQuery.
The dataset typically includes:
Usage date
Project ID
Service description
SKU
Cost
Credits and discounts
Example table:
gcp_billing_export_v1_<BILLING_ACCOUNT_ID>
4. Preparing Cost Data in BigQuery
4.1 Aggregate Daily Spend
Forecasting models work best with time-series data.
CREATE OR REPLACE TABLE cost_daily AS
SELECT
DATE(usage_start_time) AS usage_date,
SUM(cost) AS daily_cost
FROM
`billing_dataset.gcp_billing_export_v1_xxxx`
GROUP BY usage_date
ORDER BY usage_date;
4.2 Handle Missing Dates
Ensure there are no gaps in the time series. Missing dates can distort predictions.
-- Fill missing dates if needed
5. Choosing a Model in BigQuery ML
BigQuery ML supports time-series forecasting using:
ARIMA_PLUS
ARIMA_PLUS_XREG (with external regressors)
For cost forecasting, ARIMA_PLUS is usually sufficient.
6. Training the Forecasting Model
6.1 Create an ARIMA Model
CREATE OR REPLACE MODEL gcp_cost_forecast
OPTIONS(
MODEL_TYPE = 'ARIMA_PLUS',
TIME_SERIES_TIMESTAMP_COL = 'usage_date',
TIME_SERIES_DATA_COL = 'daily_cost',
AUTO_ARIMA = TRUE,
DATA_FREQUENCY = 'DAILY'
) AS
SELECT
usage_date,
daily_cost
FROM
cost_daily;
BigQuery ML automatically:
Detects seasonality
Tunes parameters
Handles trend and noise
7. Evaluating Model Performance
7.1 Model Evaluation
SELECT *
FROM ML.EVALUATE(MODEL gcp_cost_forecast);
Key metrics:
Mean Absolute Error (MAE)
Root Mean Squared Error (RMSE)
Mean Absolute Percentage Error (MAPE)
Lower values indicate better accuracy.
8. Forecasting Future GCP Spend
8.1 Generate Forecasts
SELECT *
FROM ML.FORECAST(
MODEL gcp_cost_forecast,
STRUCT(30 AS horizon, 0.9 AS confidence_level)
);
This predicts:
Daily cost for the next 30 days
Upper and lower confidence bounds
9. Monthly and Project-Level Forecasting
9.1 Monthly Aggregation
CREATE OR REPLACE TABLE cost_monthly AS
SELECT
FORMAT_DATE('%Y-%m', usage_date) AS month,
SUM(daily_cost) AS monthly_cost
FROM cost_daily
GROUP BY month
ORDER BY month;
9.2 Forecast by Project or Service
You can train separate models per project or service:
CREATE OR REPLACE MODEL project_cost_forecast
OPTIONS(
MODEL_TYPE = 'ARIMA_PLUS',
TIME_SERIES_TIMESTAMP_COL = 'usage_date',
TIME_SERIES_DATA_COL = 'daily_cost',
TIME_SERIES_ID_COL = 'project_id'
) AS
SELECT
usage_date,
project_id,
SUM(cost) AS daily_cost
FROM billing_table
GROUP BY usage_date, project_id;
10. Incorporating External Factors (Advanced)
Use ARIMA_PLUS_XREG to include regressors such as:
User growth
Traffic volume
Deployment count
Marketing events
This improves forecast accuracy during rapid scaling.
11. Visualization and Monitoring
Recommended Tools
Looker / Looker Studio
BigQuery BI Engine
Scheduled Queries for alerts
Example:
Alert when forecasted spend exceeds budget
Compare actual vs predicted cost daily
12. Best Practices
Use daily granularity for short-term forecasts
Retrain models regularly (weekly or monthly)
Separate baseline usage from spikes
Track credits and committed use discounts
Combine forecasts with budget alerts
13. Limitations
Forecasts assume historical patterns continue
Sudden architecture changes reduce accuracy
Credits and one-time charges can introduce noise
BQML works best when combined with cloud cost governance practices.
14. Summary
Using BigQuery ML to estimate and forecast GCP spend enables:
SQL-only machine learning
Scalable cost forecasting
Proactive budget control
No external ML infrastructure
With minimal setup, organizations can turn billing data into actionable cost predictions and avoid unexpected cloud expenses.
Learn GCP Training in Hyderabad
Read More
Building a Custom Billing Reconciliation System in GCP
Analyzing Cloud Storage Usage and Cost with BigQuery
Reducing Dataflow Costs Through Resource Fine-Tuning
Using Labels and Tags for Department-Wise Cost Attribution
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