All Services

Analytics

AWS Clean Rooms

AWS Clean Rooms provides governed collaboration workspaces where organizations analyze collective datasets without sharing raw underlying records with one another, using configurable analysis and privacy-enhancing controls.

Explore pricing models, common use cases, infrastructure support, and the AWS services that commonly work with AWS Clean Rooms.

AWS Clean Rooms pricing and cost programs

Pricing model: Collaboration and analysis usage

On-Demand
Available
Reserved Instances or reserved capacity
Not applicable
Savings Plans
Not applicable
Spot
Not applicable

Billing dimensions: Clean Rooms Processing Units · ML training and inference · Privacy-enhancing features

Programs and modes: SQL analysis · Clean Rooms ML · Differential privacy · Cryptographic Computing

Analysis, ML, and privacy capabilities have distinct compute dimensions.

Free Tier: Service-specific — verify current offers

Pricing reviewed 2026-07-25. Reviewed against the linked official AWS pricing page. Recheck regional rates and program terms before purchase.

Official AWS pricing

Official AWS sources reviewed 2026-07-21.

Why implement AWS Clean Rooms?

  • Lets collaborators join and analyze governed datasets without copying raw tables into another member's account or exposing unrestricted queries.
  • Provides analysis rules, configured tables, collaboration roles, supported identity matching and privacy-enhancing options to constrain queries and outputs.
  • Supports advertising, measurement, research, and partner-insight workflows with AWS Glue Data Catalog integration and auditable control-plane activity.

How to implement AWS Clean Rooms

  1. Document the legitimate purpose, parties, legal terms, permitted datasets, query owner, allowed outputs, re-identification risks, retention, audit, and termination process before technical setup.
  2. Create a collaboration and memberships, grant least-privilege service roles, configure catalog tables without unnecessary columns, and define aggregation, list, custom, or other supported analysis rules with minimal functions and output thresholds.
  3. Test allowed and adversarial queries against synthetic data, inspect results and logs, apply differential privacy or other controls where appropriate, monitor member and query activity, and rehearse revocation and collaboration closure.

AWS Clean Rooms best practices

  • Perform collaborator due diligence and privacy threat modeling, establish contractual restrictions, and do not treat Clean Rooms technical controls as proof that a collaboration is legally or ethically acceptable.
  • Expose only necessary columns and functions, set aggregation constraints on sensitive dimensions, use the strictest suitable analysis rule, consider differencing and side-channel attacks, and isolate collaborations with different purposes.
  • Use least-privilege roles and encrypted sources or results, monitor CloudTrail and collaboration activity, review rules whenever data or members change, suppress unsafe small outputs, and revoke access promptly at the end of purpose.

AWS Clean Rooms use cases and server impact

  • Privacy-conscious audience overlap and measurement
  • Partner analytics without exchanging raw records
  • Multi-party research and aggregate insight generation

Replaces custom neutral-party query environments and many raw-data exchange pipelines, but privacy analysis, legal agreements, source-data quality, allowed-query design, and re-identification risk remain human governance responsibilities.

Official implementation resources

Commonly paired AWS services