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ClickHouse Cloud vs. Snowflake: What drives the real-time performance-per-dollar gap
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A controlled benchmark measured ClickHouse Cloud’s end-to-end real-time analytics cost and latency against Snowflake’s recommended stack under identical workloads.
Real-time analytics systems must ingest and query data simultaneously. The gap shows how architecture choices in ingestion, query-readiness, and compute scaling affect cost and latency. Engineers building low-latency pipelines can use the results to evaluate trade-offs between managed services.
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Both systems ingested 113.2 billion rows at 1 million rows per second while serving interactive queries.
ClickHouse Cloud’s fresh-data path kept query latency low without additional compute, while Snowflake’s bottleneck persisted even with larger warehouses.
The 412× performance-per-dollar gap stems from differences in how each system prepares and serves data during continuous ingestion.
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The benchmark compared ClickHouse Cloud and Snowflake under identical real-time conditions. Both systems ingested 113.2 billion stock-market quotes at 1 million rows per second while serving aggregate and drill-down queries. The test isolated the fresh-data path, ingestion, query-readiness work, and query serving, using the same schema, sorting keys, and pre-aggregations. This boundary ensured the comparison measured how each system handled continuous workloads, not just peak query performance.
ClickHouse Cloud’s architecture maintained low query latency throughout ingestion. Its background merges processed millions of rows per second, keeping active partitions manageable and CPU utilization stable. Snowflake’s recommended stack, including Snowpipe Streaming and Interactive Materialized Views, introduced a bottleneck that larger warehouses could not resolve. The gap widened because Snowflake’s query engine had to perform more work per query as fresh data arrived.
Performance per dollar diverged sharply due to how each system allocated resources. ClickHouse Cloud used 16 CPUs for query serving and 4 CPUs for ingestion, with no additional serverless costs. Snowflake’s metered fresh-data path included separate serverless services, increasing total cost. The 412× gap reflects both lower latency and lower compute spend for the same workload, not just raw throughput.
The results highlight trade-offs in managed real-time analytics. ClickHouse Cloud’s integrated ingestion and query engine reduced overhead, while Snowflake’s decoupled components added flexibility but increased cost. Engineers must weigh these differences against their latency requirements and budget. The benchmark does not cover bulk loads or batch processing, where Snowflake’s strengths may shift the comparison.
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