Why Databricks can’t match ClickHouse Cloud for real-time analytics

CostBench compared ClickHouse Cloud with Databricks Serverless SQL during continuous ingestion of 113.2 billion stock-market quotes, using each platform’s recommended ingestion, data-layout, and materialized-view features while running scheduled aggregate and drill-down queries. Under its modeled list-price methodology, the benchmark reported that ClickHouse Cloud had lower preparation costs ($28.69 versus $695.60), lower normalized query costs (about $0.05 versus $2.65), and less cumulative query runtime (56.39 seconds versus 29.10 minutes), producing a claimed 752× end-to-end performance-per-dollar advantage. The comparison attributes the difference largely to ClickHouse performing ordering and incremental pre-aggregation during ingestion, whereas the tested Databricks configuration used separate Zerobus ingestion, asynchronous liquid clustering, and incremental materialized-view refresh processes, which could leave newly ingested data unclustered or summaries temporarily stale. ClickHouse’s summaries were updated with inserted data, while Databricks refresh completions were observed roughly 1.8 minutes apart, although the study notes that this is a proxy for freshness rather than a direct measure of data lag. Both systems used comparable nominal 16-CPU read-side capacity, disabled result caching, and processed the same workload, but the assessment excluded Databricks’ beta Lakehouse//RT product and omitted such costs as idle warehouse capacity, storage, networking, post-ingestion maintenance, and full provider invoices.

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