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DataOS vs K2View Fabric

Both stand on data fabric software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.

DataOS

Paid APILinuxSelf-hosted+1

DataOS combines reusable data products with governance, access and multiple consumption paths. Prospective customers need to contact the company to discuss their requirements and pricing.

Visit DataOS

K2View Fabric

Free trial APILinuxSelf-hosted+2

K2View Fabric combines entity-centered data organization with integration, governance, and deployment choices for enterprise use. Trial users start with a predefined scenario before connecting their own sources.

Visit K2View Fabric

The facts

DataOSK2View Fabric
Free planNot publishedNot published
Open sourceNot publishedNot published
Runs onAPI, Linux, Self-hosted, WebAPI, Linux, Self-hosted, Web, Windows
Lowest paid planFree or on requestFree or on request
Plans published21
MakerNot publishedNot published
Websitethemoderndatacompany.comk2view.com
On data fabric software#9 of 25#5 of 25

DataOS: what it does well

  • Can layer over existing infrastructure without migration.
  • Data products are accessible through REST, GraphQL and SQL.
  • Supports MCP exposure for AI tools and assistants.
  • Lists integrations including Snowflake, BigQuery and Tableau.
  • Enterprise plan has no usage or functionality limits.

What to know first

  • Pricing is available on request.
  • Pay-as-you-go rates and usage limits are unspecified.
  • Enterprise is a yearly subscription.

K2View Fabric: what it does well

  • Organizes data into entity-specific Micro-Databases.
  • Supports multiple integration methods, including APIs and streaming.
  • Offers multi-cloud, on-premise, and hybrid deployment.
  • Includes governance, catalog, lineage, and virtualization capabilities.
  • Free 30-day trial demonstrates a Customer 360 scenario.

What to know first

  • Pricing is available on request.
  • Trial begins with a predefined City Mobility project.
  • Connecting company sources comes after the sample scenario.

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