Which is more cost-effective for querying JSON: BigQuery or Snowflake?

BigQuery is typically more cost-effective for JSON queries due to columnar storage and scanning cost model. BigQuery charges only for data scanned, and its columnar format reads only accessed fields from JSON, significantly reducing costs. Snowflake charges for compute time regardless of data scanned. For sparse JSON queries accessing few fields, BigQuery costs less. BigQuery automatically optimizes JSON field access using statistics. Snowflake requires warehouse runtime charges even for simple queries. However, Snowflake caching provides free repeated queries while BigQuery rescans data. For complex aggregations, Snowflake clustering can be more efficient. BigQuery BI Engine provides cached query results reducing costs further. Partition and cluster your BigQuery tables on common JSON fields to minimize scan costs. Snowflake auto-suspend saves costs between queries. For infrequent large scans, BigQuery wins. For frequent complex queries, Snowflake caching might be cheaper. Test your JSON structures with our JSON Editor at jsonconsole.com/json-editor when designing schemas. Calculate actual costs based on your query patterns and data volume. Neither is universally cheaper—cost depends on access patterns.