AWS Clean Rooms
Data clean room software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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Pay-as-you-go
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- Agriculture, fishing, and forestry
- Energy and utilities
- Transportation and logistics
What is AWS Clean Rooms
AWS Clean Rooms is a managed data clean room service that enables multiple parties to analyze and collaborate on combined datasets without sharing or copying underlying raw data. It targets organizations that need privacy-preserving measurement, audience overlap analysis, and joint analytics across partners, business units, or data providers. The service runs within the AWS environment and uses configurable collaboration rules and query controls to limit what participants can access and export.
Managed privacy-controlled collaboration
AWS Clean Rooms provides a managed framework for multi-party analytics where each participant keeps control of its own data. Collaboration settings and query controls help restrict joins, outputs, and data movement to reduce the risk of exposing sensitive records. This supports common clean room use cases such as measurement and overlap analysis without requiring parties to exchange raw datasets.
Native AWS data integration
The product integrates with AWS data services and identity/access controls, which can simplify deployment for teams already operating on AWS. Organizations can keep data in their existing AWS storage and analytics environment rather than exporting to a separate platform. This can reduce operational overhead compared with building custom clean room workflows from scratch.
Flexible analytics via SQL
AWS Clean Rooms supports analytical workflows that rely on SQL-style querying and controlled result sharing. This can suit data engineering and analytics teams that want repeatable, auditable analysis patterns rather than one-off file exchanges. It also enables different collaboration configurations depending on partner requirements and governance constraints.
AWS-centric deployment model
AWS Clean Rooms is designed to operate within AWS, which can be a constraint for organizations standardized on other cloud ecosystems or on-prem environments. Cross-cloud or multi-cloud collaboration may require additional data movement, duplication, or integration work. This can increase complexity when partners do not share AWS as a common platform.
Requires technical implementation effort
Clean room collaborations typically require data preparation, schema alignment, and governance configuration before analysis is possible. Teams often need data engineering and security expertise to set up permissions, collaboration rules, and approved queries. Non-technical marketing users may need additional tooling or internal support to operationalize recurring workflows.
Use-case scope depends on controls
The privacy and output controls that make clean rooms useful can also limit certain downstream actions, such as exporting granular datasets or running unrestricted modeling. Some advanced activation or identity-resolution workflows may require complementary services outside the clean room. As a result, organizations may need additional components to cover end-to-end data collaboration and activation requirements.
Plan & Pricing
Pricing model: Pay-as-you-go (usage-based)
Free tier/trial: Free Plan is not available (see Free Tier note). No time-limited free trial mentioned on the official pricing page.
Compute (CRPU-hour) pricing (examples shown for US East - N. Virginia):
- Spark SQL compute: $2.00 per CRPU-hour (price varies by region). CRPU billed per-second with a 60-second minimum charge. Example: default 32 CRPUs -> 4.8 CRPUs/day example = $9.60/day. Feature notes: Differential Privacy adds $2.00 per CRPU-hour (total $4.00/CRPU-hour when enabled).
- PySpark compute: Examples on the page use $4.00 per CRPU-hour (price varies by region). PySpark measures compute in CRPU-hours, billed per-second with a 10-minute (0.167 hour) minimum charge per job. Default allocation is 32 CRPUs; configurable from 8 up to 256 CRPUs (and multiple instance types CR.1X / CR.4X).
AWS Clean Rooms ML pricing (custom & lookalike):
- Custom modeling (training/inference): Records pricing charged on a price-per-million-records basis (examples shown on the page). Example for training: $0.01 per 1,000 records (page example: 30,000,000 records = $300). Inference example shows tiered record pricing in the example: $10.00 per million for the first 1,000 million, then $0.20 per million thereafter (page example: 50 billion records = $19,800). Custom modeling also charges for instance compute (e.g., ml.p3.8xlarge) and Spark SQL compute used to prepare input data.
- Synthetic dataset generation compute: $2.00 per SDGU (Synthetic Data Generation Unit). Example: 400 SDGUs/month = $800.
- Lookalike modeling: $0.04 per 1,000 profiles for training dataset; $0.25 per 1,000 profiles for lookalike segment generation. Example: 50M profiles training = $2,000; 10 segments of 2M profiles = $5,000.
AWS Entity Resolution on AWS Clean Rooms pricing:
- Data preparation: $0.10 per 1,000 records processed.
- Data matching (rule-based): $0.50 per 1,000 records matched + one-time $100 matching fee per collaboration (the fee is charged to the collaborator paying for matching).
- Data service provider–based matching: $0.10 per 1,000 records processed (requires provider subscription; pricing does not include third-party provider fees).
Additional costs / notes:
- Query results and data I/O use Amazon S3 and AWS Glue Data Catalog; standard S3 and Glue charges apply in addition to AWS Clean Rooms charges.
- Pricing can vary by AWS Region and certain features (PySpark, ML, Entity Resolution) are not covered by AWS Free Tier.
Example costs pulled from official page:
- PySpark example (campaign measurement): 5.3 CRPUs * $4.00/CRPU-hour = $21.33 monthly (3-minute job with 10-minute min).
- Spark SQL example (default CR.1X 16 instances): 4.8 CRPUs * $2.00/CRPU-hour = $9.60 daily.
- Synthetic data generation example: 400 SDGUs/month * $2.00/SDGU = $800/month.
- Lookalike modeling example: 50M profiles * $0.04 per 1,000 = $2,000 (training); 10 segments of 2M profiles * $0.25 per 1,000 = $5,000 (segments).
Discount options: Not specified on the AWS Clean Rooms pricing page; pricing notes that costs vary by region. Contact AWS sales for enterprise/volume/commitment pricing options.
Minimum-charge details (documented on page):
- PySpark: 10-minute minimum charge per PySpark job (0.167 hours).
- Spark SQL: 60-second minimum charge per Spark SQL query.
(Information sourced solely from the official AWS Clean Rooms pricing page.)
Seller details
Amazon Web Services, Inc.
Seattle, Washington, USA
2006
Subsidiary
https://aws.amazon.com/
https://x.com/awscloud
https://www.linkedin.com/company/amazon-web-services/
