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Databend vs Greenplum Database

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Databend

8.2#1 in analytical data store softwareFrom $23 a month
Free planWebAPILinuxmacOS
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What both publish

FactDatabendGreenplum Database
IntegrationsDatabend says it has more than 50 integrations spanning relational databases, streams and orchestrators, lake formats, cloud storage, visualization tools, languages and drivers.The maker describes streaming ingestion with Tanzu Greenplum Streaming Server on Kubernetes and a certified standalone Apache NiFi connector for Greenplum 7.
LicenseThe repository is Apache 2.0, while a small set of enterprise features uses the Elastic 2.0 license.The project repository states that Greenplum Database is released under the Apache 2 license.
ProductDatabend describes itself as an open-source, Rust-powered, cloud-native warehouse for analytics, search, and AI workloads.Greenplum Database is an open-source data warehouse based on PostgreSQL for analytics on petabyte-scale data.
StorageThe homepage lists S3, Azure, GCS, OSS, R2, MinIO, HDFS, and HF as storage options.It supports append-optimized row or column storage, and its column-oriented storage can be compressed.
SupportThe company page advertises 24/7 support, while plan details specify tier-specific response commitments.The project repository directs users to online documentation and its wiki for further information.

Databend plans

  • CommunityFree
  • Databend CommunityFree
  • Business$23 a month
  • Personal$23 a month
  • DedicatedPriced on request

Greenplum Database plans

  • Greenplum Database (open source)Free
  • VMware Tanzu GreenplumPriced on request

Pick Databend if…

Databend suits developers and organizations building analytics, search, or AI workloads that need an open-source warehouse and can choose between self-hosted and cloud deployment. Its documented ingestion pipelines and storage options may also suit teams connecting varied data systems.

Pick Greenplum Database if…

Greenplum Database is suited to organizations building large analytical warehouses or business intelligence workloads that need distributed query processing and external data access. Its documented use cases also include enterprise analytics, AI, vector and RAG workloads, and IoT applications.

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