How to Estimate and Control BigQuery SaaS Pricing

How can I accurately forecast and control BigQuery SaaS costs?

BigQuery pricing is pay-as-you-go for storage and bytes processed, with optional flat-rate reservations to cap spend.

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Description

What drives BigQuery SaaS pricing?

BigQuery charges for two main resources: data storage (bytes stored per month) and analysis (bytes processed by queries). Optional costs include streaming inserts, data transfers, and flat-rate reservations. Understanding which component dominates your workload lets you optimise spend proactively.

How do I estimate query costs before running?

Use EXPLAIN or dry-run queries with the BigQuery API to return total_bytes_processed without executing. Multiply that figure by the on-demand analysis price (currently $5 per TB).This prevents surprise bills and encourages engineers to tune queries early.

How to track storage costs in BigQuery?

Query INFORMATION_SCHEMA.TABLE_STORAGE to list active and long-term storage for every table. Group by dataset or project to see which business unit owns the biggest footprint and prune, partition, or cluster accordingly.

Can I cap BigQuery spend with flat-rate reservations?

Yes. BigQuery editions let you purchase slots hourly or commit yearly for predictable pricing.Calculate the breakeven point by dividing your average monthly on-demand analysis bill by the flat-rate cost—switch once on-demand consistently exceeds the reservation price.

Best practices to control BigQuery costs

Partition and cluster large tables, filter early, avoid SELECT *, cache results, use materialised views for repeat logic, schedule automatic table expiration, and monitor jobs with Cloud Monitoring budgets or custom dashboards.

Why How to Estimate and Control BigQuery SaaS Pricing is important

How to Estimate and Control BigQuery SaaS Pricing Example Usage


-- Estimate last-month revenue query cost (dry-run)
DECLARE job ANY TYPE;
SET job = (
  SELECT *
  FROM  (CALL
            bq.query_job(
                query => '''SELECT c.id, c.name, SUM(p.price*oi.quantity) AS lifetime_value
                              FROM Customers c
                              JOIN Orders o   ON o.customer_id = c.id
                              JOIN OrderItems oi ON oi.order_id   = o.id
                              JOIN Products p   ON p.id          = oi.product_id
                              WHERE c.created_at >= DATE_SUB(CURRENT_DATE(), INTERVAL 1 YEAR)
                              GROUP BY c.id, c.name''',
                dry_run => TRUE))
);
SELECT job.total_bytes_processed/1e12 AS estimated_TB,
       job.total_bytes_processed/1e12*5 AS estimated_cost_usd;

How to Estimate and Control BigQuery SaaS Pricing Syntax


-- Dry-run to estimate query cost
bq query --use_legacy_sql=false --dry_run \
'WITH revenue AS (
   SELECT o.id, SUM(oi.quantity * p.price) AS order_total
   FROM Orders o
   JOIN OrderItems oi ON oi.order_id = o.id
   JOIN Products p ON p.id = oi.product_id
   WHERE o.order_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
   GROUP BY o.id)
SELECT COUNT(*) FROM revenue;'

-- Check storage per table
SELECT table_schema,
       table_name,
       ROUND(size_bytes/1e9,2) AS size_gb
FROM   `region-us`.INFORMATION_SCHEMA.TABLE_STORAGE
ORDER  BY size_bytes DESC;

Common Mistakes

Ignoring dry-run estimates leads to unexpected analysis bills. Always run queries with --dry_run or EXPLAIN first to know bytes processed.
Storing cold data indefinitely increases storage spend. Move tables older than 90-days to long-term storage, archive to Cloud Storage, or set table expiration.