ClickHouse Cloud vs. Snowflake: What drives the real-time performance-per-dollar gap

CostBench compared ClickHouse Cloud and Snowflake under continuous ingestion of 113.2 billion stock-market quotes, using the same schema, workload, approximate 16-CPU query-serving capacity, sorting or clustering keys, and target ingest rate of one million rows per second. The benchmark found that ClickHouse Cloud integrated asynchronous ingestion, ordering, incremental pre-aggregation, and background merging within its storage engine, keeping raw data and aggregate summaries synchronized, while Snowflake’s Snowpipe Streaming ingestion and Interactive Materialized View refresh operated as separate services, leaving the view an average of 1.4 minutes behind during active ingestion. According to the results, Snowflake’s current aggregate queries then required query-time reconciliation of unrefreshed data, with compilation accounting for 85% of aggregate-query time, whereas querying raw tables avoided reconciliation but required more scanning and aggregation work. ClickHouse reported substantially lower aggregate latency, a smaller advantage on drill-down queries, and lower fresh-data-path costs; the benchmark’s combined cost-and-runtime scoring model calculated ClickHouse Cloud’s end-to-end real-time performance per dollar as 412 times better than the tested Snowflake configuration. The analysis also notes Snowflake alternatives involving asynchronously refreshed tables and dedicated refresh warehouses, which can improve query speed but introduce additional cost and a minimum freshness lag.

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