BigQuery Cost Optimization
Why BigQuery Costs Creep Up
BigQuery charges per byte scanned ($5/TB for on-demand). As your data grows, queries that were cheap become expensive. A query scanning 4TB costs $20 per run — run it 10 times a day and that's $200/day or $6,000/month for a single query. Most teams don't notice until the monthly bill arrives.
Common Expensive Patterns
SELECT * (scans all columns), missing partition pruning, correlated subqueries (forces nested scans), unnecessary JOINs on large tables, lack of clustering, and querying raw tables instead of materialized views. These patterns often develop gradually as datasets grow.
Partition and Cluster Strategy
Always partition by date/timestamp columns — this is the single biggest cost lever. Cluster by your most common filter columns (up to 4). Use _PARTITIONTIME pseudo-column for ingestion-time partitioning. Require partition filters in project settings to prevent full scans.
Query Refactoring Techniques
Select only needed columns. Replace correlated subqueries with JOINs. Use approximate aggregation functions (APPROX_COUNT_DISTINCT is 95% cheaper). Pre-aggregate in materialized views. Use LIMIT with ORDER BY on non-partitioned queries. Cache results with query results caching.
Flat-Rate vs On-Demand
On-demand ($5/TB) works for <100TB/month scanned. Above that, consider flat-rate slots — $2,000/month for 100 slots gives predictable pricing. BigQuery Editions offer autoscaling slots for variable workloads. Use Cloptima to analyze your query patterns and recommend the optimal pricing model.
How Cloptima Helps
Cloptima's AI Query Optimization analyzes your BigQuery query history, identifies expensive patterns, and provides concrete refactored queries with estimated cost savings. We've seen customers reduce BigQuery spend by 40-80% with our suggestions. No competitor offers this capability — most tools just show you the bill.