---
title: "ClickHouse Cloud vs. Snowflake: What drives the real-time performance-per-dollar gap"
description: "The benchmark compares ClickHouse Cloud with Snowflake under a continuous real-time workload, asking whether ingested data becomes query-ready quickly enough for interactive analytics. Both systems received the same 113.2-billion-row NBBO dataset at a target of 1 million rows per second, with matched sorting or clustering keys, pre-aggregations, query schedules, and approximately 16 CPUs of query-serving compute. ClickHouse used native asynchronous inserts and maintained ordering and pre-aggregation during ingestion, while Snowflake used Snowpipe Streaming and Interactive MV refresh as separately metered services. In the end-to-end CostBench comparison, ClickHouse Cloud delivered 412× better performance per dollar: Snowflake’s path cost 3.32× more and its matched queries took 124× longer in total. The comparison attributes the gap to query-readiness work, refresh lag, reconciliation, and growing drill-down latency."
---

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

[Clickhouse](https://yomu.fyi/company/clickhouse) · Sep 10, 2026

**Type:** Benchmark

## Summary

The benchmark compares ClickHouse Cloud with Snowflake under a continuous real-time workload, asking whether ingested data becomes query-ready quickly enough for interactive analytics. Both systems received the same 113.2-billion-row NBBO dataset at a target of 1 million rows per second, with matched sorting or clustering keys, pre-aggregations, query schedules, and approximately 16 CPUs of query-serving compute. ClickHouse used native asynchronous inserts and maintained ordering and pre-aggregation during ingestion, while Snowflake used Snowpipe Streaming and Interactive MV refresh as separately metered services. In the end-to-end CostBench comparison, ClickHouse Cloud delivered 412× better performance per dollar: Snowflake’s path cost 3.32× more and its matched queries took 124× longer in total. The comparison attributes the gap to query-readiness work, refresh lag, reconciliation, and growing drill-down latency.

## Context

The benchmark examines real-time analytics under continuous ingestion, where incoming data must become query-ready while aggregate and drill-down queries continue running. It focuses on how fresh-data-path work affects both operating cost and query latency.

## Approach / What changed

The comparison used the same 113.2-billion-row NBBO dataset, schema, workload, schedule, and paced ingestion target for both systems. ClickHouse used native asynchronous inserts with ordering and pre-aggregation during ingestion; Snowflake used Snowpipe Streaming and Interactive MV refresh. Query-serving compute, layouts, and the complete metered fresh-data path were matched as closely as the platforms allowed.

## Takeaways

- ClickHouse Cloud delivered 412× better performance per dollar in the end-to-end CostBench comparison.
- Snowflake’s end-to-end path cost 3.32× as much as ClickHouse Cloud, while its matched queries took 124× longer in total.
- Snowflake’s aggregate paths required reconciliation against lagging materialized views or raw-table scans for current answers; ClickHouse kept raw and pre-aggregated data synchronized during ingestion.

**Tags:** [ClickHouse](https://yomu.fyi/topic/clickhouse), [Performance](https://yomu.fyi/topic/performance), [Snowflake](https://yomu.fyi/topic/snowflake), [Streaming](https://yomu.fyi/topic/streaming)

- Source: [Clickhouse](https://clickhouse.com/blog/clickhouse-vs-snowflake-real-time-performance-per-dollar)
- Source URL: https://clickhouse.com/blog/clickhouse-vs-snowflake-real-time-performance-per-dollar
- Ingested by Yomu: 2026-09-09T08:00:50.245Z

[Read original post](https://clickhouse.com/blog/clickhouse-vs-snowflake-real-time-performance-per-dollar)