When to use Clean Rooms

Databricks offers two complementary collaboration modes: OpenSharing and Clean Rooms. Understanding which to use — and how to combine them — is one of the most common questions partners face.

The two modes

OpenSharing

An open protocol for secure, zero-copy sharing of data and AI assets across platforms. The recipient runs their own analytics in their own environment.

Best for: Delivering tables, models, features, or notebooks to consumers who run their own workloads. One-to-many data distribution. Standard publisher → consumer patterns.

Clean Rooms

A governed joint compute environment where multiple parties run analytics and AI on combined data — without any party seeing the others' raw records.

Best for: Joint analysis where both sides contribute data, where IP protection is required, or where strict privacy controls prevent simple data delivery.

Decision guide

Scenario	Use
I want to deliver data to many consumers	OpenSharing
I need to run analytics on combined data from both sides	Clean Rooms
I need to protect my algorithms while using customer data	Clean Rooms (with private libraries)
I want to offer a recurring collaboration service	Clean Rooms (subscription model)
My customers are on different clouds	Clean Rooms (cross-cloud, no replication)

Combining the two modes

These modes work best together. Common patterns:

Try-before-you-buy

Stand up a Clean Room where prospects can explore a sample or production subset of your data under strict privacy rules. Let them validate schema, join logic, and business value without exporting raw datasets. When they are ready, graduate them to OpenSharing for ongoing delivery.

Premium services tier

Use OpenSharing for standard table delivery to all customers, and offer Clean Rooms as a premium add-on for customers who need joint computation — fraud analytics, audience enrichment, attribution modeling, etc.

What Clean Rooms are not

• Not a substitute for OpenSharing when the use case is straightforward data delivery — use OpenSharing alone for that
• Not an anonymization tool — Clean Rooms control who runs what code on what data, but it is still your responsibility to share minimally necessary or masked datasets
• Not required for single-party workloads — if only one party contributes data and logic, standard OpenSharing or in-tenant compute is simpler

What's next

• Review use cases by industry, including productization patterns for packaging Clean Rooms as a repeatable product
• Understand the architecture before setting up your first clean room