How to Evaluate a Data Clean Room: What Separates the Platforms

Data clean room platforms split along one axis more than any other: whether the privacy guarantee is enforced by contract and access controls, or enforced at the hardware level so that not even the platform operator can see raw inputs. That distinction, along with how the platform prices activation and how well it fits into infrastructure you already run, does more to separate the options than any feature checklist.

What job is a clean room hired to do?

A data clean room lets two or more parties compute on combined data, overlap analysis, audience matching, joint measurement, without either party seeing the other's raw records. What a clean room is built to solve is now reasonably well understood across the buy side; the harder question is which implementation fits a specific data-sharing relationship, since "clean room" now covers everything from a hosted SaaS product to a feature bolted onto a general-purpose data warehouse.

Five things distinguish the platforms in practice:

How do the platforms differ?

Decentriq enforces its privacy guarantee at the hardware layer, using confidential computing (Intel SGX and AMD SEV trusted execution environments) so that data is encrypted during computation and only the chip itself can decrypt it, a claim the company backs by being a founding member of the Confidential Computing Consortium. Its onboarding model needs only the initiating party to hold a platform license; partners join through a single-license setup that removes per-counterparty procurement, which the company cites as a reason large agency trading desks, cited cases include Publicis and Omnicom, have adopted it for managing many simultaneous clean room programs. Access is API-first with a no-code interface layered on top, and pricing runs on an annual license (median cited at roughly $10,000 per month) or, on the sell side, revenue share tied to media value. Walled garden participation is out of scope by design, a limit shared across the category, though Decentriq has a documented non-public measurement integration with Meta that gives it access to user-level exposure data for measurement purposes specifically, an integration few clean room vendors hold.

Optable built its clean room, branded Collaborate, as part of a broader identity and data-collaboration platform rather than as a standalone product. Its distinguishing mechanic is the Flash node, which lets a partner's first-party or brand data get onboarded into a collaboration without a direct contract between the two parties, reducing the setup friction that usually accompanies a new data-sharing relationship. Commercially, Optable charges no per-destination or per-activation fees and prices on data volume and processing rather than media spend, with proof-of-concept engagements typically running about three months before ongoing pricing is set from observed usage. Optable's own identity graph draws on a roughly 700-million-record partnered dataset for enrichment and match, which supports its audience-building use cases but means clean room buyers evaluating privacy-control specifics, query-level access limits, minimum aggregation thresholds, should confirm current documentation directly, since public detail on those controls is thinner than on the identity side of the platform.

Databricks approaches clean rooms as a native extension of its lakehouse platform rather than a purpose-built privacy product. Clean Rooms run on top of Delta Sharing and Unity Catalog, giving column- and row-level access policy enforcement and cross-cloud sharing across AWS, Azure, and GCP through an open sharing protocol. The collaboration interface is a shared notebook environment, which puts the workflow closer to a data engineering team's normal tools than a marketer's, and pricing follows Databricks' standard DBU consumption model rather than a flat clean room license. Because Databricks isn't purpose-built for advertising use cases, walled garden connections and packaged measurement templates, incrementality, MMM feeds, aren't native; buyers get them by building custom pipelines on top of the platform instead.

Where buyers get it wrong

The most common mistake is assuming "clean room" means the same privacy guarantee across every vendor. Contractual and access-policy enforcement, common across much of the category, is meaningfully different from hardware-enforced isolation, where the platform operator has no technical ability to see raw data regardless of policy. Buyers in regulated industries or with sensitive data (health, finance, or data covered by strict cross-border rules) should ask specifically which model a vendor uses rather than accepting "clean room" as a self-certifying label.

The second mistake is underestimating onboarding friction on the partner side. A platform that requires every participant to buy a license adds real time to standing up a new collaboration, especially for agencies or retailers running many simultaneous partnerships. Single-license or no-contract onboarding models exist specifically to solve that, and the value is proportional to how many partners a buyer expects to bring in over the life of the contract.

The third mistake is expecting walled garden data to flow through any clean room the same way open-web data does. Meta, Google, and Amazon do not export raw user-level data to third-party clean rooms for audience building or targeting, a design boundary common to the category rather than a gap specific to any one vendor. Measurement-specific exceptions exist but are narrow and vendor-specific.

How should the decision get made?

Start with who else needs to sit at the table. An agency managing clean room relationships across many brands and partners benefits disproportionately from a single-license onboarding model, since the procurement overhead compounds with every new counterparty. A team with a hard, auditable privacy requirement, health data, financial data, cross-border personal data, should weight hardware-enforced isolation over policy-based access control, since the guarantee doesn't depend on configuration being done correctly. A team already standardized on a specific cloud data warehouse should weigh how much a native, notebook-based clean room saves against a dedicated product built for non-engineering users.

CartographAI runs independent, free assessments of clean room platforms and adjacent data infrastructure tools, scoring vendors across the same dimensions covered here so buyers can compare documented capability rather than sales-deck claims.

A few names worth evaluating beyond the platforms discussed above, non-exhaustive: InfoSum, Snowflake, Habu, LiveRamp, and AWS Clean Rooms all show up regularly in this category, spanning identity-first, warehouse-native, and retailer-specific approaches. The field is larger than this list, and the right fit depends heavily on which side of the walled-garden boundary, and which cloud, the collaboration needs to sit on.

FAQ

Is a data clean room the same thing as a data warehouse with permissions? Not quite. A data warehouse with row-level permissions restricts who can see which rows; a clean room additionally lets two separate organizations compute jointly on data neither party fully exposes to the other, typically returning only aggregated or query-limited outputs rather than raw record access.

Can a clean room connect to Meta, Google, and Amazon directly? Generally not for raw data exchange. The major walled gardens do not export user-level data to third-party clean rooms for targeting or audience building; audiences built elsewhere can typically be pushed to those platforms as activation destinations, and a small number of vendors hold narrow, vendor-specific measurement integrations.

Do we need hardware-enforced privacy, or is policy-based access control enough? It depends on the sensitivity of the data and the regulatory exposure. Policy-based access control is sufficient for many commercial use cases and is far more common across the category. Hardware-enforced isolation matters most when the data involved is regulated (health, finance) or when a buyer needs a guarantee that doesn't depend on configuration being maintained correctly over time.

How is clean room usage typically priced? Pricing models vary: flat annual licenses, usage-based pricing tied to data volume or compute, and revenue-share arrangements on the sell side all appear in the category. Buyers should confirm whether pricing scales with the number of partner collaborations, since that variable affects total cost more than raw data volume in many multi-partner deployments.

Does a clean room replace an identity resolution platform? No, though several vendors bundle both. A clean room's job is privacy-safe joint computation; identity resolution's job is matching records across sources into a single profile. Some platforms, including a few discussed above, offer identity resolution and clean room capability together, but buyers with a narrow need should evaluate each capability against its own requirements rather than assuming a combined product is stronger at either job individually.