Published ClickHouse user stories show why teams adopted the platform and what they had to engineer around it. Use the examples to identify questions worth testing on your own data.
The summaries below describe the reported use case. Follow each source for its architecture, scope, and limitations.
Luzmo uses ClickHouse Cloud for embedded analytics across varied customer datasets. Its story describes moving from self-managed ClickHouse to Cloud to reduce infrastructure administration.
Attentive describes simplifying a fragmented analytics architecture with ClickHouse Cloud. The team used representative workloads, parallel systems, and query comparisons while moving services incrementally.
ClickHouse reports that Respan uses Cloud for LLM observability at roughly 50 million events per day. Its architecture uses focused schemas and aggregations to support analytical queries.
Scale and results from another organization are context, not a forecast. Use the differences to sharpen your evaluation.
Look at data shape, row size, retention, query mix, tenant distribution, freshness, and concurrent users. A shared industry does not mean a shared workload.
Include ingestion, transformations, pre-aggregation, caching, application behavior, and the people operating the system.
Agree the performance, correctness, reliability, and cost conditions that would make the change worthwhile in your environment.
Bring that use case and the ways your environment differs. We can turn the comparison into a focused assessment or pilot.