AWS Clean Rooms vs InfoSum Data Clean Room
AWS Clean Rooms
aws.amazon.comInfoSum Data Clean Room
infosum.comSide by side
| Figure | AWS Clean Rooms | InfoSum Data Clean Room |
|---|---|---|
| Price from | — | — |
| Free plan | — | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| API | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Data Clean Room Software | 7.3 (#10) | 7.4 (#8) |
| Founded | 2006 | 2015 |
| Integrations | AWS names Snowflake, LiveRamp, Neustar, and Experian among its AWS Clean Rooms partners. | Listed activation destinations include Amazon S3, Google Cloud Storage, Snowflake Beacon Table, Google PAIR, and Customer Match. |
| Purpose | AWS Clean Rooms lets companies analyze and collaborate on collective datasets without sharing or copying one another’s underlying data. | The Data Clean Room matches and analyzes unlimited datasets without sharing or moving data. |
| Support | The AWS Clean Rooms site provides an option to contact an AWS Clean Rooms expert. | The support page lists [email protected] and phone numbers +1 877-266-3700 and +44 (0)808 2812768. |
| Use cases | AWS lists advertising campaign insights, investment decisions, research and development, customer insights, and reporting among its use cases. | The product supports audience insights, identity translation, targeting segments, and campaign measurement. |
Plans
In their own words
AWS Clean Rooms
AWS Clean Rooms lets companies analyze collective datasets without exchanging or copying the underlying data. Multiple companies can contribute to one collaboration while retaining data in their own accounts. Customers can permission AWS data and work with partner datasets in Snowflake and AWS through zero-ETL. Analysis supports SQL, Spark SQL, PySpark and machine learning. AWS lists advertising insights, investment decisions, research and development, customer insights and reporting as use cases. Privacy controls include fine-grained analysis rules, differential privacy, cryptographic computing, output restrictions and query logging. Clean Rooms ML supports custom and lookalike modeling; custom modeling allows collaborators to bring a model for training and inference without sharing underlying data or intellectual property. Access is available through the console, API, SDKs and command line. AWS lists availability in 11 Regions. Pricing is pay-as-you-go by capability and Region, with stated minimum charges for Spark SQL and PySpark. No free plan is available.
InfoSum Data Clean Room
InfoSum Data Clean Room matches and analyzes datasets across organizations without transferring the underlying data. Advertisers, publishers, data owners, commerce media businesses, data providers, and media owners can use first-party data for audience insights, identity translation, targeting, planning, activation, and campaign measurement. InfoSum describes its processing as decentralized and cloud agnostic, using non-movement technology and differential privacy. Personal identifiers are salted and hashed, data is encrypted at rest in decentralized Bunkers, and query results are aggregated with differential privacy techniques. Marketers have drag-and-drop tools and simple queries, while dataset owners can set, change, or revoke permissions. Data can be held in an InfoSum Bunker or a Beacon in a customer cloud or warehouse. Listed destinations include S3, sFTP, Google Cloud Storage, Snowflake Beacon Table, Google PAIR, Ads Data Manager, Customer Match, and DataX. The API supports data onboarding for accounts with an API key and appropriate settings and permissions. Pricing is available on request, and users must arrange platform access through InfoSum rather than self-registering.