DataOS vs IBM Cloud Pak for Data

DataOS is a data activation layer that adds shared context, governance, and intelligence over an organization’s existing data stack. Its reusable data products combine data, transformation logic, semantic models, quality contracts, access policies, governance, and consumption APIs. Products are available through REST, GraphQL, and SQL, and can be exposed through MCP for AI tools and assistants. Listed capabilities include observability, data quality, data sharing, a knowledge graph, a built-in lakehouse, governance, a distributed SQL workbench, and an automatic data catalog. Named connections include AWS S3, Snowflake, Google BigQuery, Kafka, Jupyter Notebooks, Tableau, and Looker. DataOS can layer over infrastructure without migration, keep data where it resides, and deploy in any cloud environment. Security listings include attribute-based access control, multifactor authentication, encryption, audit trails, and alerts. Pricing is on request: pay-per-use rates and limits are unspecified, while Enterprise is yearly with no usage or functionality limits.

IBM Cloud Pak for Data has no write-up on Specifiction yet; its figures are above.