The Warehouse Sizing Paradox: Why I Sometimes Choose XL Over Small
“Always use the smallest warehouse possible to save money.”
I heard this advice constantly when I started with Snowflake. It sounds logical—smaller warehouses cost less per hour, so naturally they should be cheaper, right?
Except the math doesn’t always work that way.
Here’s what I’ve observed:
Snowflake charges by the second with a 60-second minimum. The cost difference between warehouse sizes is linear, but the performance difference often isn’t.
The actual formula is simple:
Total Cost = Credits per Second × Runtime in Seconds
A Small warehouse might be 1/4 the cost per second of an XL, but if it takes 5x longer to complete the same query, you’re paying more overall.
When I’ve seen this matter most:
Working with subscription and order data, certain query patterns consistently benefit from larger warehouses:
→ Customer lifetime value calculations across millions of subscribers
→ Daily cohort analysis with complex retention logic
→ Product affinity analysis joining order details with high SKU cardinality
→ Aggregating subscription events over multi-year periods
These workloads benefit dramatically from parallelization. An XL warehouse has 8x the compute resources of an XS, and for the right queries, it can complete them in less than 1/8th the time.
A simple experiment you can run:
— Test with Small warehouse
USE WAREHOUSE small_wh;
SELECT SYSTEM$START_QUERY_TIMER();

SELECT
    subscription_plan,
    customer_segment,
    COUNT(DISTINCT customer_id) as subscribers,
    SUM(order_value) as total_revenue,
    AVG(order_value) as avg_order_value,
    COUNT(DISTINCT order_id) as total_orders
FROM orders
WHERE order_date >= ‘2023-01-01’
GROUP BY subscription_plan, customer_segment
HAVING COUNT(DISTINCT customer_id) > 100;

— Note the execution time and credits used in query profile

— Now test with XL warehouse
USE WAREHOUSE xl_wh;
— Run the same query
Check the query profile for each:
Execution time
Credits consumed (Execution Time × Warehouse Size Credits/Second)
Total cost
The decision framework I use:
Size up when:
Query runtime > 2 minutes on current warehouse
Query profile shows high parallelization potential
You’re running the query repeatedly (daily pipelines)
Spillage to remote disk is occurring
Stay small when:
Queries are simple lookups or filters
Runtime is already under 30 seconds
Workload is highly sequential (limited parallelization)
It’s truly ad-hoc, one-time analysis
The nuance that surprised me:
It’s not just about individual query cost—it’s about total warehouse utilization. If your Small warehouse runs 10 queries in 100 minutes, but an XL runs them in 20 minutes, you’re paying for 80 fewer minutes of warehouse time. That matters when you’re paying for auto-suspend delays, concurrent users, or just opportunity cost.
My practical approach:
I start with Medium for most workloads. Then I profile:
Queries consistently taking 3+ minutes → test on Large or XL
Queries under 1 minute → consider downsizing to Small
Monitor credit consumption patterns weekly
The goal isn’t to find the “right” size—it’s to match warehouse size to workload characteristics.
Want to test this yourself?
Here’s a quick query to see your warehouse credit consumption:
SELECT
    warehouse_name,
    SUM(credits_used) as total_credits,
    COUNT(*) as query_count,
    AVG(execution_time)/1000 as avg_seconds,
    SUM(credits_used)/COUNT(*) as credits_per_query
FROM snowflake.account_usage.query_history
WHERE start_time >= DATEADD(day, -7, CURRENT_TIMESTAMP())
    AND warehouse_name IS NOT NULL
GROUP BY warehouse_name
ORDER BY total_credits DESC;
This shows you which warehouses are consuming credits and whether you might benefit from right-sizing.
The counterintuitive truth:
The cheapest warehouse per hour isn’t always the cheapest warehouse per result. Sometimes spending more per second means spending less overall.
What’s been your experience with warehouse sizing? Have you found scenarios where bigger was actually cheaper?
#Snowflake #DataEngineering #CostOptimization #CloudDataWarehouse