Right-Sizing Warehouses: When Bigger Isn't Better

Right-Sizing Warehouses: When Bigger Isn't Better

Many users default to X-Large warehouses when Small would suffice. Understand when to scale up (and more importantly, when to scale down).

The Problem

Organizations often over-provision warehouses "just to be safe" or because they don't understand Snowflake's architecture. An X-Large warehouse costs 16x more than an X-Small, but doesn't always run queries 16x faster.

When to Use Larger Warehouses

• Complex joins on large datasets
• Aggregations over billions of rows
• Concurrent users requiring high throughput
• ETL processes with time constraints

When Smaller is Better

• Simple queries (key lookups, single-table scans)
• Small result sets
• Low concurrency workloads
• Development and testing

The Diminishing Returns Curve

Doubling warehouse size doesn't double query performance:

• X-Small to Small: 70-80% faster
• Small to Medium: 60-70% faster
• Medium to Large: 50-60% faster
• Large to X-Large: 40-50% faster

Right-Sizing Strategy

1. Start with Small or Medium warehouses
2. Monitor query performance
3. Scale up only if queries are consistently slow
4. Use multi-cluster for concurrency, not single large warehouses

Real-World Example

A data team was using X-Large warehouses for all workloads "for consistency." Analysis showed that 80% of queries completed in under 10 seconds and didn't benefit from the larger size. By right-sizing to Small/Medium warehouses with multi-cluster enabled, they reduced warehouse costs by 60% while maintaining the same query performance.

Key Takeaways

• Start small and scale up only when needed
• Larger warehouses have diminishing returns
• Use multi-cluster for concurrency
• Monitor actual query performance vs warehouse size