Data clean rooms & privacy-enhancing tech · Amazon Web Services

AWS Clean Rooms

AWS-native clean room for SQL and Spark analysis across parties' data, with an optional differential-privacy add-on.

Visit AWS Clean Rooms Alternatives

AWS Clean Rooms lets two or more organizations query each other's data with SQL or PySpark without either party copying the underlying tables out of its own AWS account; results, not raw rows, are what participants see, and each collaborator sets analysis rules controlling what queries are allowed. Its differential-privacy option adds calibrated statistical noise to query outputs, at roughly double the base compute price, to bound how much any single query can reveal about an individual record — a documented, quantifiable privacy mechanism rather than a general claim. AWS Clean Rooms ML extends the service with lookalike modeling and custom modeling jobs billed per record processed, and it integrates with AWS Entity Resolution for identity matching between parties. As an AWS-only product, it suits organizations already centered on AWS rather than multi-cloud collaboration.

At a glance

Vendor Amazon Web Services
Pricing model Usage-based
Free tier No
Deployment Cloud
Open source No
Best for AWS-centric organizations running two-party or multi-party analysis without moving data off AWS.

Pricing

Billed per Clean Rooms Processing Unit (CRPU) hour for SQL/Spark, with add-on rates for differential privacy, ML and entity resolution.

Plan Price Notes
SQL query $2.00 per CRPU-hour 60-second minimum charge per query; varies by region
SQL query with differential privacy $4.00 per CRPU-hour $2.00 base plus $2.00 differential-privacy add-on
PySpark job $4.00 per CRPU-hour 10-minute minimum charge; default allocation 32 CRPUs
AWS Entity Resolution (rule-based matching) $0.50 per 1,000 records + $100 base fee/collaboration separate from Clean Rooms compute

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features

  • SQL and PySpark analysis without copying raw data out
  • Configurable analysis rules per collaboration
  • Differential privacy add-on with quantified noise parameters
  • AWS Clean Rooms ML: lookalike and custom modeling
  • AWS Entity Resolution integration for identity matching
  • Synthetic dataset generation
  • Fine-grained IAM-based access control

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Snowflake Data Clean Rooms Clean room capability built into Snowflake's platform, keeping each party's raw data inside its own Snowflake account. LiveRamp Clean Room LiveRamp's clean room, built on the acquired Habu technology, for cross-cloud data collaboration tied to LiveRamp's identity graph. Decentriq Confidential-computing clean room that runs analysis inside hardware-encrypted enclaves so no party, including Decentriq, can see the raw data.

AWS Clean Rooms in the index now

Terms to know

First-party data Data a company collects directly from its own customers and properties, rather than acquiring it from outside sources. Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Incrementality testing An experiment measuring the true causal lift of a marketing activity by comparing it against a held-out control. Differential privacy A mathematical technique for publishing aggregate statistics while guaranteeing no individual record can be inferred from the results. Record linkage Matching records that refer to the same person or entity across datasets that lack a shared unique identifier.

Related guides

How to choose a data clean room Pick a data clean room by who you need to collaborate with, which cloud you're both on, and how strong a privacy guarantee the use case actually requires. How to measure marketing without third-party cookies First-party data, server-side collection, modeled conversions, clean rooms, MMM and incrementality tests — the methods that replace what cookies used to do.