What's the Difference Between AWS Clean Rooms and Clean Rooms ML?

Compare AWS Clean Rooms and Clean Rooms ML side by side — features, pricing, and ideal use cases to help you choose the right product.

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Compare side-by-side

Comparisons	AWS Clean Rooms	Clean Rooms ML
Category	Analytics, Data Collaboration	Analytics, Privacy-Safe ML
Description	Create clean rooms to collaborate on datasets without sharing raw data.	Apply machine learning with your Clean Rooms collaborations without sharing raw data.
Best for	Advertising analytics Cross-company data collaboration Privacy-safe analytics Audience insights	Lookalike modeling Privacy-safe ML Audience expansion Collaborative analytics
Key features	Configurable analysis rules Cryptographic computing No data movement Built-in query controls ML modeling	Lookalike models Privacy controls Clean Rooms integration No data sharing Custom models
Pricing model	Pay per query processed	Pay per model training + inference
Free tier	No	No
Expert take	“Clean Rooms lets multiple parties analyze combined datasets without sharing raw data. It enforces cryptographic controls so each participant only sees aggregate results.” — Giovanni Lauria, re:Post Top Contributor [profile]	“Clean Rooms ML enables privacy-safe machine learning across organizations. Train models on combined datasets without any party seeing the other's raw data.” — Giovanni Lauria, re:Post Top Contributor [profile]
Product page	Learn more about AWS Clean Rooms »	Learn more about Clean Rooms ML »

When to use AWS Clean Rooms or Clean Rooms ML

Use AWS Clean Rooms when:

Advertising analytics
Cross-company data collaboration
Privacy-safe analytics
Audience insights

Learn more about AWS Clean Rooms »

Use Clean Rooms ML when:

Lookalike modeling
Privacy-safe ML
Audience expansion
Collaborative analytics

Learn more about Clean Rooms ML »

Next steps with AWS for Analytics

Create a free account

Start building with AWS Clean Rooms and Clean Rooms ML on the AWS Free Tier.

Get started with AWS Clean Rooms

Create clean rooms to collaborate on datasets without sharing raw data.

Go to AWS Clean Rooms

Get started with Clean Rooms ML

Apply machine learning with your Clean Rooms collaborations without sharing raw data.

Go to Clean Rooms ML

AWS product comparisons

What's the difference between Amazon Aurora and Amazon RDS?

Both are fully managed relational database services on AWS, but Aurora is a cloud-native engine built for higher performance and availability, while RDS gives you familiar open-source and commercial engines with less operational overhead than self-managed.

What's the difference between Amazon DynamoDB and Amazon RDS?

DynamoDB is a serverless NoSQL database for applications with well-defined access patterns that need consistent single-digit millisecond performance. RDS is a managed relational database for applications that need SQL, joins, and transactional integrity across multiple engines.

What's the difference between Amazon DynamoDB and Amazon Keyspaces?

Both are fully managed, serverless-capable NoSQL databases on AWS. DynamoDB is AWS-native with its own API and data model; Keyspaces is Apache Cassandra-compatible, letting you use existing Cassandra drivers, tools, and expertise.

What are the differences between Amazon DynamoDB and Amazon DocumentDB?

Both are fully managed NoSQL databases on AWS, but they serve different data models. DynamoDB is a key-value store optimized for known access patterns at massive scale; DocumentDB is a document database for flexible JSON schemas with MongoDB compatibility.

What's the difference between Amazon DocumentDB and Amazon Neptune?

Both handle semi-structured data, but they're optimized for different query patterns. DocumentDB stores and queries JSON documents; Neptune stores and traverses relationships between entities.

What's the difference between Amazon Aurora and Amazon RDS?

Both are fully managed relational database services on AWS, but Aurora is a cloud-native engine built for higher performance and availability, while RDS gives you familiar open-source and commercial engines with less operational overhead than self-managed.

What's the difference between Amazon Aurora and Amazon Neptune?

Both are purpose-built, fully managed AWS databases, but they solve fundamentally different problems. Aurora handles structured relational data with SQL; Neptune handles highly connected data where relationships between entities are the primary query pattern.

What's the difference between Amazon Aurora and Amazon DynamoDB?

Both are fully managed AWS database services, but they serve fundamentally different data models and access patterns. Aurora is a relational database for structured data with relationships; DynamoDB is a NoSQL key-value store built for single-digit millisecond performance at any scale.

What's the difference between Amazon RDS and Amazon Redshift?

Both run SQL queries, but they're optimized for fundamentally different workloads. RDS handles transactional operations (inserts, updates, lookups) for your application. Redshift handles analytical queries (aggregations, scans, joins across billions of rows) for your data team.

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