CrateDB

Name: CrateDB
Brand: CrateDB
Rating: 4.4 (83 reviews)

CrateDB is a distributed SQL database that enables real-time analytics on high-volume, high-velocity data by combining a distributed SQL layer with Lucene-based indexing to support analytics and search-style queries over mixed relational and semi-structured data.

Est 2013|Valuation $85M|Raised $57.6M|36 Ref Customers

LinkedIn 6K|Web traffic 13K|FTEs 67

MarketsCloud Databases

01Competitive Landscape

Narrative analysis

CrateDB vs ClickHouse: - Third-party comparisons indicate ClickHouse often outperforms on raw OLAP query efficiency, while CrateDB may be selected where integration with IoT systems and mixed-schema SQL querying are priorities. These are synthesis observations rather than formal benchmark results. CrateDB vs Timescale: - CrateDB targets a broader multi-model analytics use case including document-like fields and search-style queries, while Timescale emphasizes time-series specialization and Postgres compatibility. Organizations prioritizing full Postgres behavior and extension compatibility may prefer Timescale. Market adoption signals (limited): - Some vendor-agnostic platforms report rising engagement metrics for CrateDB in certain open-source database categories; such mindshare signals are proxies based on platform engagement and are not equivalent to market share.

	CrateDB	ClickHouse	Timescale (managed Timescale Cloud)
Starting Price	Free shared plan available; cloud tiers include entry-level shared and paid dedicated compute tiers with pay-as-you-go billing	Trial and tiered paid plans with usage-based pricing options	Entry-level monthly compute tiers with additional storage charges in managed offering
Primary Use Case	Real-time analytics with SQL across mixed data types; IoT/machine data; operational analytics	High-performance OLAP analytics and large-scale analytical queries	Time-series workloads with Postgres ecosystem; hypertables and continuous aggregates
Key Strength	SQL familiarity plus distributed scaling; multi-model capabilities (time-series + documents + search-like features)	Performance efficiency and columnar OLAP strengths; mature ecosystem	Postgres compatibility and time-series specialization; strong developer familiarity
Key Weakness	Not suited for OLTP transactions; some users report Kubernetes/self-managed documentation gaps; smaller ecosystem	Less multi-model SQL feel; may require tuning and architectural decisions	Postgres-based constraints for some workloads; scaling model differences and indexing/partitioning considerations

01Competitive Landscape

Narrative analysis

CrateDB vs ClickHouse:

Third-party comparisons indicate ClickHouse often outperforms on raw OLAP query efficiency, while CrateDB may be selected where integration with IoT systems and mixed-schema SQL querying are priorities. These are synthesis observations rather than formal benchmark results.

CrateDB vs Timescale:

CrateDB targets a broader multi-model analytics use case including document-like fields and search-style queries, while Timescale emphasizes time-series specialization and Postgres compatibility. Organizations prioritizing full Postgres behavior and extension compatibility may prefer Timescale.

Market adoption signals (limited):

Some vendor-agnostic platforms report rising engagement metrics for CrateDB in certain open-source database categories; such mindshare signals are proxies based on platform engagement and are not equivalent to market share.

	CrateDB	ClickHouse	Timescale (managed Timescale Cloud)
Starting Price	Free shared plan available; cloud tiers include entry-level shared and paid dedicated compute tiers with pay-as-you-go billing	Trial and tiered paid plans with usage-based pricing options	Entry-level monthly compute tiers with additional storage charges in managed offering
Primary Use Case	Real-time analytics with SQL across mixed data types; IoT/machine data; operational analytics	High-performance OLAP analytics and large-scale analytical queries	Time-series workloads with Postgres ecosystem; hypertables and continuous aggregates
Key Strength	SQL familiarity plus distributed scaling; multi-model capabilities (time-series + documents + search-like features)	Performance efficiency and columnar OLAP strengths; mature ecosystem	Postgres compatibility and time-series specialization; strong developer familiarity
Key Weakness	Not suited for OLTP transactions; some users report Kubernetes/self-managed documentation gaps; smaller ecosystem	Less multi-model SQL feel; may require tuning and architectural decisions	Postgres-based constraints for some workloads; scaling model differences and indexing/partitioning considerations