CrateDB
CrateDB is a distributed SQL database built from the ground up for high-cardinality, multi-dimensional analytics on live operational data.
Publisher review
CrateDB is a distributed SQL database built from the ground up for high-cardinality, multi-dimensional analytics on live operational data. It is designed for teams who work with high-volume, high-cardinality data and cannot wait for ETL to catch up. Unlike a PostgreSQL extension, CrateDB uses a shared-nothing architecture where every node is equal and the cluster self-balances as data grows. It supports time-series, JSON, relational, geospatial, text, and vector data natively, without requiring separate extensions or pre-aggregation. The database runs consistently across public cloud, private cloud, on-premises, and edge environments, with fully managed cloud services on AWS, Azure, and GCP, as well as self-hosted options via CrateDB OSS (Apache 2.0) and CrateDB Enterprise.
CrateDB ingests data through continuous streaming and efficient batch ingestion, with auto-indexing in milliseconds on every field at ingestion time. Queries execute in parallel across distributed nodes, returning sub-second results on billions of raw records across 900,000 unique series without defining rollups in advance. The database uses native SQL with built-in functions and CTEs, supports columnar and row storage with sharding, partitioning, and replication, and delivers authentication, authorization, encryption, audit logging, and compliance certifications. Multiple connectivity options include the PostgreSQL wire protocol, making it compatible with most BI tools and PostgreSQL libraries.
In the market, CrateDB competes directly with TimescaleDB, InfluxDB, MongoDB, Yellowbrick Data Warehouse, and Amazon Quantum Ledger Database. Compared to TimescaleDB, which is a PostgreSQL extension requiring Continuous Aggregates for high-query-volume analytical workloads, CrateDB handles ad-hoc queries at high cardinality without pre-defined rollups. TimescaleDB recommends pre-defining query patterns for best performance, while CrateDB queries immediately on new dimensions after deployment. CrateDB also provides native vector search co-located with time-series data, whereas TimescaleDB relies on the pgvector extension. For full-text search, CrateDB uses a distributed, Lucene-based engine, while TimescaleDB depends on PostgreSQL tsvector and extensions.
The honest trade-offs: CrateDB's self-hosted deployments have limited distributed hypertables support and can experience performance degradation at high cardinality and scale. The database does not offer the full PostgreSQL ecosystem compatibility that TimescaleDB provides, since it uses the wire protocol rather than being a true PostgreSQL extension. Teams accustomed to PostgreSQL's mature tooling and extensions may need to adjust workflows. Additionally, while CrateDB Cloud offers flexible billing through cloud marketplaces, the custom plan for large production workloads requires contacting sales, and the free plan is restricted to 2 vCPUs, 2 GB RAM, and 8 GB storage.
How it works
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Distributed shared-nothing architecture
Every node is equal; the cluster self-balances as data grows. Scale by adding nodes without manual reconfiguration.
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Native multi-model data support
Stores and queries time-series, JSON, relational, geospatial, text, and vector data natively without separate extensions.
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Auto-indexing on ingestion
Every field is indexed automatically in milliseconds at ingestion time, enabling sub-second queries on raw data without pre-aggregation.
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Parallel query execution
Queries execute in parallel across distributed nodes, returning sub-second results on billions of raw records across 900,000 unique series.
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Continuous streaming ingestion
Supports both continuous streaming and efficient batch ingestion for high-throughput data ingestion without bottlenecks.
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PostgreSQL wire protocol compatibility
Uses standard SQL and the PostgreSQL wire protocol, so most BI tools and PostgreSQL-compatible libraries connect without modification.
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Flexible deployment options
Runs on public cloud (AWS, Azure, GCP), private cloud, on-premises, and edge environments with fully managed or self-hosted models.
Strengths and trade-offs
Strengths
- Handles ad-hoc queries at high cardinality on raw data without requiring pre-defined Continuous Aggregates, unlike TimescaleDB.
- Auto-indexes every field in milliseconds at ingestion time, eliminating manual index management for time-series and JSON data.
- Provides native vector search co-located with time-series data, avoiding the need for a separate pgvector extension.
- Offers a free on-premise version (CrateDB OSS) under Apache 2.0 license and a fully managed cloud free plan with 2 vCPUs, 2 GB RAM, and 8 GB storage.
Trade-offs
- Self-hosted deployments have limited distributed hypertables support compared to the cloud-managed version.
- Performance can degrade at very high cardinality and scale for self-hosted deployments, especially without Enterprise support.
- Does not provide full PostgreSQL ecosystem compatibility; it uses the wire protocol, so some PostgreSQL-specific extensions and tools may not work.
- The custom plan for large production workloads requires contacting sales, and the free plan is capped at 2 vCPUs, 2 GB RAM, and 8 GB storage.
Pricing context
CrateDB Cloud has a free plan (2 vCPUs, 2 GB RAM, 8 GB storage), a Shared plan from $0.073/hour, a Dedicated plan from $0.246/hour (up to 9 nodes, 144 vCPUs, 495 GB RAM, 72 TB storage), and a Custom plan with unlimited nodes and custom compute. On-premise: CrateDB OSS is free (Apache 2.0); CrateDB Enterprise requires a paid license.
Getting started with CrateDB
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Sign up for CrateDB Cloud
Go to the CrateDB Cloud website and create a free account. Choose the free plan with 2 vCPUs, 2 GB RAM, and 8 GB storage to start. Verify your email and log in to the console.
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Connect your data source
In the CrateDB Cloud console, select your cluster and note the connection string. Use any PostgreSQL-compatible client or library to connect, providing the host, port, database name, and credentials from the console.
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Create a table for time-series data
Run a CREATE TABLE statement defining columns for your time-series data, such as timestamp, sensor_id, and value. Use the CLUSTERED INTO clause to distribute data across shards and PARTITION BY for efficient querying.
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Ingest sample data via INSERT
Insert a few rows of sample data using INSERT statements to verify connectivity and schema. For example, insert a timestamp, a sensor ID, and a measurement. Confirm the data appears with a SELECT query.
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Run an ad-hoc analytical query
Execute a SELECT query with GROUP BY and aggregation functions on the ingested data. For instance, compute average values per sensor over a time range. Observe sub-second response without pre-defined rollups.
Frequently Asked Questions
What is CrateDB and how does it work?
CrateDB is a distributed SQL database built for high-cardinality, multi-dimensional analytics on live operational data. It uses a shared-nothing architecture where every node is equal and the cluster self-balances as data grows, supporting time-series, JSON, relational, geospatial, text, and vector data natively.
How does CrateDB compare to TimescaleDB?
CrateDB handles ad-hoc queries at high cardinality without pre-defined rollups, unlike TimescaleDB which requires Continuous Aggregates for best performance. CrateDB also offers native vector search co-located with time-series data, while TimescaleDB relies on the pgvector extension for vector capabilities.
What is CrateDB's pricing and free plan?
CrateDB Cloud offers a free plan with 2 vCPUs, 2 GB RAM, and 8 GB storage. Shared plans start at $0.073/hour, Dedicated plans from $0.246/hour up to 9 nodes, and Custom plans for unlimited nodes. On-premise, CrateDB OSS is free under Apache 2.0, while Enterprise requires a paid license.
How does CrateDB handle high-cardinality data without pre-aggregation?
CrateDB auto-indexes every field in milliseconds at ingestion time, enabling sub-second queries on billions of raw records across 900,000 unique series without defining rollups in advance. Queries execute in parallel across distributed nodes, returning results on raw data without pre-aggregation.
What deployment options does CrateDB offer?
CrateDB runs on public cloud (AWS, Azure, GCP), private cloud, on-premises, and edge environments. It offers fully managed cloud services and self-hosted options via CrateDB OSS (Apache 2.0) and CrateDB Enterprise with a paid license for on-premise deployments.
What are the limitations of CrateDB's self-hosted version?
Self-hosted CrateDB has limited distributed hypertables support compared to the cloud version. Performance can degrade at very high cardinality and scale without Enterprise support. It also lacks full PostgreSQL ecosystem compatibility since it uses the wire protocol, not a true PostgreSQL extension.
Alternatives
How CrateDB compares
Direct head-to-head against 3 competitors. Picked by 7wData.
CrateDB
- Pricing
- CrateDB Cloud has a free plan (2 vCPUs, 2 GB RAM, 8 GB storage), a Shared plan from $0.073/hour, a Dedicated plan from $0.246/hour (up to 9 nodes, 144 vCPUs, 495 GB RAM, 72 TB storage), and a Custom plan with unlimited nodes and custom compute. On-premise: CrateDB OSS is free (Apache 2.0); CrateDB Enterprise requires a paid license.
- Target
- CrateDB is a distributed SQL database built from the ground up for high-cardinality, multi-dimensional analytics on live operational data.
- Strength
- Handles ad-hoc queries at high cardinality on raw data without requiring pre-defined Continuous Aggregates, unlike TimescaleDB.
- Watch for
- Self-hosted deployments have limited distributed hypertables support compared to the cloud-managed version.
TimescaleDB
- Pricing
- Self-hosted free; cloud from $30/month for 10GB storage
- Target
- PostgreSQL teams needing time-series with moderate cardinality
- Deployment
- Self-hosted, cloud
- Strength
- PostgreSQL extension with 200+ hyperfunctions for time-series
- Watch for
- Performance degrades at high cardinality; requires pre-defined aggregates
InfluxDB
- Pricing
- Free tier (limited); cloud from $0/hour; self-hosted open source
- Target
- DevOps and IoT teams focused on time-series metrics
- Deployment
- Self-hosted, cloud
- Strength
- Purpose-built time-series database with Flux query language
- Watch for
- Limited SQL support; not suited for multi-dimensional ad-hoc analytics
ClickHouse
- Pricing
- Self-hosted free; cloud from $0.10/hour per node
- Target
- Teams needing real-time analytics on large event datasets
- Deployment
- Self-hosted, cloud
- Strength
- Columnar storage optimized for high-throughput analytical queries
- Watch for
- Complex cluster management; no native full-text search
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