Estimate data warehouse costs from storage, query volume, and insert operations. Budget for BigQuery, Redshift, or Snowflake.
Cloud data warehouses like BigQuery, Snowflake, and Redshift have transformed analytics, but their pricing models can be confusing. Costs come from three main components: storage (per TB retained), compute/queries (per TB scanned or per credit consumed), and data ingestion (per operation or per TB loaded). Understanding how these three pillars combine is essential for accurate budgeting.
This calculator breaks down data warehouse costs into storage, query processing, and insert/ingestion components. Enter your storage volume in TB, average monthly query volume in TB scanned, and ingestion operation counts. The tool calculates the total monthly cost and shows which component dominates your bill.
Whether you're evaluating a migration from on-premises to cloud, comparing Snowflake vs. BigQuery, or optimizing an existing warehouse, this calculator helps you model costs and identify savings opportunities.
This measurement provides a critical foundation for capacity planning and performance budgeting, helping teams align infrastructure resources with application requirements and growth projections.
Data warehouse bills routinely surprise organizations because query costs are unpredictable. This calculator models all three cost pillars—storage, compute, and ingestion—so you can budget accurately and identify which component to optimize first. Having accurate metrics readily available streamlines incident postmortems, architecture reviews, and technology roadmap discussions with engineering leadership and product teams.
storage_cost = storage_TB × storage_rate; query_cost = query_TB × query_rate; insert_cost = (inserts / 1000) × insert_rate; total = storage_cost + query_cost + insert_cost
Result: $175.00/month
Storage: 5 TB × $20 = $100/month. Queries: 10 TB scanned × $5/TB = $50/month. Inserts: 500,000 ops / 1,000 × $0.05 = $25/month. Total: $175/month. Query cost accounts for 29% of the bill—partitioning tables could reduce scanned data by 50%+.
Modern warehouses use columnar compression that reduces storage by 3–10× compared to row-based databases. However, the storage rate applies to the compressed size. Partitioning by date allows you to drop old partitions without scanning the entire table.
The biggest lever for query cost reduction is reducing data scanned. Partition pruning skips irrelevant partitions. Clustering sorts data within partitions for even tighter pruning. Column projection (SELECT only needed columns) reduces bytes read in columnar stores.
Batch loads are cheaper than streaming inserts. Accumulate data in staging storage (S3, GCS) and load in hourly or daily batches. Streaming inserts provide lower latency but cost more per row. Choose based on your freshness requirements.
BigQuery charges $0.02/GB/month for storage (first 10 GB free) and $5/TB for on-demand queries (first 1 TB/month free). Flat-rate plans start at $2,000/month for 500 slots. Streaming inserts cost $0.01/200 MB.
Snowflake separates storage ($23/TB/month) from compute (credits). Credits cost $2–$4 each depending on edition. A Small warehouse uses 1 credit/hour. Costs depend on how long warehouses run and their size.
Redshift Serverless charges per RPU-hour (~$0.375/RPU-hour). Provisioned Redshift charges per node-hour ($0.25–$13.04/hr depending on node type). Storage is included with RA3 nodes at $0.024/GB/month for managed storage.
Scanning large unpartitioned tables, using SELECT *, and running ad-hoc queries across full datasets. Partition pruning, column selection, and materialized views can reduce scanned data by 50–90%.
Partition and cluster tables. Use materialized views for repeated queries. Suspend idle compute. Set query byte limits to prevent runaway queries. Archive old data to cheaper storage. Schedule large batch jobs during off-peak windows.
If your query volume is predictable and steady, reserved/flat-rate pricing saves 30–60%. If your workload is bursty and unpredictable, on-demand pricing avoids paying for idle capacity. Many organizations use a mix of both.