How to choose a data clean room

A data clean room lets two or more organizations analyze their combined data together without either side handing over its raw records. A retailer and a brand can measure whether an ad campaign drove purchases without the retailer exposing its full customer list to the brand, or the brand exposing its media data to the retailer — each party keeps its own data, and only approved, usually aggregated, results leave the room. Teams need one whenever a partnership requires joint analysis across a data boundary that neither party is willing, or legally able, to cross with raw data. A single organization analyzing its own data, with no second party involved, doesn't need this category at all — that's just a data warehouse with normal access control.

Infrastructure clean rooms vs. martech clean rooms

The tools here split into two groups with different default audiences:

• Cloud-native, general-purpose clean rooms. AWS Clean Rooms and Snowflake Data Clean Rooms are built into a cloud platform's own infrastructure. Either works for any two datasets that live on that platform — advertising, healthcare, fraud analysis, anything — and the deciding question is simply whether both collaborators already store data there.
• Advertising and identity-focused clean rooms. InfoSum, Optable, and LiveRamp Clean Room are built specifically for the advertising ecosystem — audience overlap, campaign measurement, and identity resolution tied to a matching layer (LiveRamp's RampID, InfoSum's decentralized "bunker" matching, Optable's identity graphs). These come with advertising-specific templates and DSP/SSP integrations that a general-purpose infrastructure clean room does not.

Cross-industry, cross-platform collaboration that needs to work regardless of which cloud either party is on — healthcare, financial services, cross-institution fraud analysis — points toward a third category: confidential-computing clean rooms like Decentriq, discussed below.

Where does your raw data actually go

This is the question that matters most, and the tools give genuinely different answers:

• With AWS Clean Rooms and Snowflake Data Clean Rooms, each party's raw data stays inside its own account on that platform; templated, governed queries run across both and only approved results are returned. Neither AWS nor Snowflake — nor the counterparty — sees the other side's raw rows, but the mechanism is query governance and result aggregation, not encryption of the data during computation.
• Decentriq makes a stronger, more specific claim: computation runs inside hardware-based encrypted enclaves (confidential computing), so the vendor operating the clean room states it cannot see the raw data either — not just the counterparty. This matters when the sensitivity of the data rules out even a trusted third-party operator seeing it, which is why Decentriq is positioned toward healthcare, financial services, and pharma collaboration.
• InfoSum's architecture keeps each partner's data in its own environment permanently and matches on mathematical representations of records rather than moving or pooling raw data at all — a decentralized model rather than a shared computation environment.

If your compliance or legal team asks "can the vendor see our data," the answer differs meaningfully across these products — get it in writing, not from a sales deck.

Differential privacy vs. aggregation thresholds

Two different privacy mechanisms show up under similar marketing language. Aggregation thresholds — a minimum group size a query result must contain before it's released — are the default in most of these tools (Snowflake, LiveRamp) and are simple but only as strong as the threshold set. Differential privacy, offered as an add-on in AWS Clean Rooms, adds calibrated statistical noise to outputs with a mathematically quantified guarantee bounding what any single query can reveal about an individual record — a stronger, more specific claim, and one that roughly doubles compute cost when enabled. If a use case is sensitive enough that "differential privacy" appears in a compliance requirement, confirm the vendor supports it explicitly, not just aggregation.

How pricing works

Every tool in this category is usage-based or quote-only; none publishes flat per-seat pricing, because cost depends on data volume and query complexity, not headcount. AWS Clean Rooms is the one tool with a public, itemized rate card (per compute-unit-hour, with a separate add-on rate for differential privacy); Snowflake's clean room capability is billed through standard Snowflake compute credits with no separate line item. The martech-focused tools (InfoSum, Optable, LiveRamp, Decentriq) are quoted per engagement — expect the quote to reflect data volume, number of collaborating parties, and whether identity resolution is included.

A shortlist by situation

• If both parties are already on the same cloud platform (AWS or Snowflake), start with that platform's native clean room before adding a third-party product.
• If you're running advertising audience overlap or campaign measurement and want a purpose-built identity layer, look at LiveRamp Clean Room (multi-cloud, tied to RampID) or InfoSum (decentralized matching, no raw data movement).
• If you're a publisher or advertiser building first-party audience collaboration specifically around the decline of third-party cookies, look at Optable.
• If the use case is in healthcare, finance, or another setting where even the vendor must not see raw data, look at Decentriq's confidential-computing model.
• If you need multi-cloud collaboration where the two parties aren't on the same platform, LiveRamp Clean Room and Decentriq are both built for that; the cloud-native options are not.

Questions to ask vendors

• Can you see our raw data at any point during computation, and can that be confirmed technically, not just contractually?
• What privacy mechanism governs outputs — aggregation thresholds, differential privacy, or both — and who sets the thresholds?
• Does this work if our counterparty is on a different cloud platform than we are?
• How is a collaboration billed — per query, per compute-hour, per record matched, or a flat engagement fee — and who pays?
• What happens to query templates and approval logs; is there an audit trail for compliance review?

Common mistakes

• Assuming "clean room" implies a single, consistent privacy guarantee across vendors — the mechanisms differ meaningfully.
• Choosing a martech-focused clean room for a non-advertising use case and finding the templates and integrations don't fit.
• Picking a cloud-native option without confirming the other party is actually on that same cloud.
• Treating aggregation thresholds and differential privacy as interchangeable when a compliance requirement specifically calls for one.

For head-to-head detail, see AWS Clean Rooms vs Snowflake Data Clean Rooms and InfoSum vs LiveRamp Clean Room. Every tool in this category is listed at /tools/category/data-clean-rooms-privacy-enhancing-tech/.