Compare

ClickHouse vs StarRocks

ClickHouse favors a simple single-binary architecture for loaded data; StarRocks queries lakehouse tables in place alongside its own storage.

Side by side

ClickHouse StarRocks
Vendor ClickHouse, Inc. StarRocks (Linux Foundation project; commercial backing from CelerData)
Pricing model Open source + paid options Open source + paid options
Free tier Yes Yes
Deployment Cloud, Self-hosted Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0)
Best for Engineering teams building real-time dashboards or log/event analytics who want maximum query throughput on commodity hardware. Teams wanting a single fast SQL engine across both freshly loaded data and existing lakehouse tables.
Pricing

The self-hosted database is free open source; the managed ClickHouse Cloud service is billed separately (see clickhouse-cloud).

Pricing has not been verified yet — see the vendor's site.

Free and open source; a commercial managed cloud version is offered separately by CelerData.

Pricing has not been verified yet — see the vendor's site.

Features
  • Columnar storage with vectorized execution
  • Sub-second aggregate queries over billions of rows
  • Materialized views for pre-aggregation
  • Native Kafka, S3 and MySQL/Postgres table engines
  • High compression ratios via column-level codecs
  • Distributed sharding and replication
  • Standard SQL dialect with array and JSON extensions
  • Vectorized execution with cost-based optimizer
  • Query federation across Iceberg, Hudi, Delta Lake and Hive
  • Materialized views that accelerate lakehouse queries
  • MySQL wire-protocol compatibility
  • Real-time streaming ingestion
  • Shared-data architecture separating compute and storage

Verdict

Both are open-source, vectorized, columnar engines aimed at sub-second aggregate queries, but they favor different assumptions about where your data lives. ClickHouse favors a simpler, single-binary architecture and is most commonly used with data loaded natively into it, maximizing query speed on its own storage. StarRocks — which shares its origins with Apache Doris and is now a Linux Foundation project with commercial backing from CelerData — was built to query data in place across Iceberg, Hudi, Delta Lake and Hive tables as a first-class capability alongside its native storage, avoiding a separate ingestion step when the data already lives in a lakehouse.

Choose ClickHouse if

  • Your workload is primarily data loaded natively — events, logs, metrics — rather than federated across lakehouse tables.
  • You want the simplest single-binary operational model in this category, with fewer distinct process types to run.
  • You want the largest, most battle-tested community for real-time OLAP, with a managed option in ClickHouse Cloud if you'd rather not self-host.

Choose StarRocks if

  • A meaningful share of your queries need to hit Iceberg, Hudi, Delta Lake or Hive tables directly, without copying them into a separate store first.
  • You want materialized views that specifically accelerate queries against lakehouse tables.
  • You're already comparing it against Apache Doris, its closest relative, and want Linux Foundation governance plus CelerData's commercial support.

What they share

Both are Apache-2.0 licensed, use vectorized columnar execution, target sub-second aggregate queries at scale, and are self-hosted first, each with a commercial managed-cloud option from a vendor built around the project — ClickHouse Cloud from ClickHouse, Inc., and CelerData for StarRocks.

The honest caveat

If lakehouse federation is the deciding factor for your evaluation, it's worth looking at Apache Doris alongside this pair — it shares StarRocks's lineage and similar federated-query capability, and the three are frequently shortlisted together rather than as a strict binary choice.

Last reviewed September 22, 2026

In the index now