StarRocks alternatives

3 tools to consider instead of StarRocks, shown against it.

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

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.

Free and open source under the Apache Software Foundation; a commercial cloud version is offered separately by SelectDB.

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

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; commercial managed and enterprise-supported distributions are sold separately by Starburst.

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

Features
  • 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
  • Real-time streaming and micro-batch ingestion
  • MySQL wire-protocol compatibility
  • Federated queries over Hive, Iceberg and Hudi
  • Materialized views and automatic query rewrite
  • Vectorized execution engine
  • High-concurrency point lookups alongside large scans
  • 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
  • Federated SQL queries across heterogeneous data sources
  • Pluggable connector architecture (Iceberg, Hive, Kafka, JDBC sources)
  • Massively parallel, in-memory distributed execution
  • ANSI SQL compatibility
  • Cost-based query optimizer
  • Fine-grained access control via connectors

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