Plan query budgets using scan, compute, and egress. Tune assumptions for peak load and caching. Get clear per query and monthly totals in seconds.
| Scenario | Queries/day | Scanned (GB) | Compute (s) | Cache hit | Returned (GB) | Notes |
|---|---|---|---|---|---|---|
| Dashboards | 12,000 | 8 | 2.5 | 60% | 0.05 | High cache reuse and small outputs. |
| Ad-hoc analysis | 1,500 | 120 | 15 | 10% | 0.25 | Large scans with low reuse. |
| ETL validation | 3,500 | 35 | 7 | 25% | 0.15 | Moderate scans, some caching. |
1) Convert scan price to per GB: scan_price_per_gb = scan_price_per_tb ÷ 1024
2) Apply caching: cache_miss = (100 − cache_hit_rate) ÷ 100
3) Effective usage per query:
4) Cost per query:
5) Monthly estimate: final_monthly = base_per_query × monthly_queries × (1−discount%) × (1−commit%) × (1+overhead%)
Query spend is usually shaped by three measurable drivers: data scanned, compute time, and bytes delivered. This calculator keeps those drivers separate so you can see which one dominates. Start by pulling averages from your query history, then model a realistic peak hour using the concurrency and peak multipliers.
Most warehouses expose scanned bytes, execution time, and result size per query. Convert bytes to gigabytes, group by workload type, and pick a representative percentile for planning. For example, use p50 for baseline and p90 for peak. When caching is enabled, measure hit rate by counting repeated query signatures.
Scan-based services charge mainly for read volume, so reducing scanned gigabytes often produces the fastest savings. Compute-based models emphasize vCPU and memory seconds, so tightening timeouts, limiting joins, and right-sizing workers can reduce cost. The engine profile setting shifts weight to reflect these patterns.
Egress charges can surprise teams that export large results to external tools or different clouds. Use the external egress percentage to reflect what actually leaves your network boundary. If you share data across regions, combine the region multiplier with higher egress pricing to stress-test collaboration plans.
Discounts and commitments reduce billed cost, but they should be applied after you trust the base model. Keep a version of your estimate with zero discounts to understand true efficiency. Then add negotiated and committed savings, and include support overhead to reflect monitoring, incident response, and governance work.
Start with scanned gigabytes and execution seconds from query logs. Those two fields usually explain most variance in cost. Add result size next if you export data outside your network frequently.
Compare repeated query patterns over a week and calculate what fraction is served from cache. If you cannot measure it directly, run scenarios at 0%, 25%, 50%, and 75% to bracket impact.
Use 1.0 for steady traffic. If you see slower performance or higher billed time during peak, try 1.1 to 1.5. For batch windows with many parallel jobs, test 1.5 to 2.5.
Providers price regions differently due to capacity, power, and network costs. A multiplier lets you compare placements consistently. Keep it aligned with your invoice rate cards when you have them.
Accuracy depends on the quality of your averages and peak assumptions. When you feed it real measured inputs, it is useful for forecasting directionally and for comparing scenarios, not for exact billing reconciliation.
Reduce scanned data with partitioning, clustering, and selective columns. Improve caching by reusing prepared datasets. Limit result sizes, avoid exporting large tables, and right-size compute slots for sustained loads.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.