Cinchy Data Fabric vs DataOS
Cinchy Data Fabric
cinchy.com
DataOS
themoderndatacompany.comSide by side
| Figure | Cinchy Data Fabric | DataOS |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 2 |
| Spec score · Data Fabric Software | 7.5 (#14) | 7.6 (#11) |
| Deployment | Cinchy can run in a customer’s AWS, Azure, or GCP environment, in Cinchy’s managed cloud, or on premises with air-gap capability. | The maker says DataOS can be deployed in any cloud environment and adapts to existing infrastructure. |
| Headquarters | Toronto, Ontario, Canada | Palo Alto, California |
| Integrations | Cinchy provides data connectors for synchronizing data between sources and destinations, and says its connector list is evolving. | The maker’s FAQ names AWS S3, Snowflake, Google BigQuery, Kafka, Jupyter Notebooks, Tableau, and Looker as supported connections or integrations. |
| Intended users | Cinchy describes the platform for regulated and data-intensive industries, including financial services, technology, and manufacturing. | The maker describes DataOS as serving business teams, data teams, and AI agents, with business users able to find and use trusted data without engineering support. |
| Security | Cinchy describes its Data Collaboration Platform as SOC 2 certified and says governance and security are embedded in the platform. | The FAQ lists attribute-based access control, multi-factor authentication, encryption in transit and at rest, audit trails, and automated alerts. |
Plans
In their own words
Cinchy Data Fabric
Cinchy Data Fabric has no write-up on Specifiction yet; its figures are above.
DataOS
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.