The best data clean room companies in 2026

Enterprise data strategy has reached a point of maturity where "privacy-by-design" is a fundamental legal requirement rather than a strategic choice. For modern organizations, the challenge has shifted from justifying the need for a data clean room (DCR) to selecting a permanent architectural foundation for high-stakes collaboration.

This guide is designed for Heads of Data, VP Analytics, Heads of Adtech, head of Martech, Head of data monetization, or any other stakeholders who need to shortlist and evaluate the current vendor landscape from a technical standpoint. We have evaluated six leading providers — including Decentriq, Snowflake, AWS, InfoSum, LiveRamp, and Databricks — across six critical criteria: privacy architecture, deployment model, governance controls, interoperability, multi-party capability, and operational fit.

Key takeaways: The 2026 landscape

• The shift: The industry now has options between policy-based privacy (trusting a contract) and technical-based privacy (trusting the technology and hardware).
• The lens: This guide evaluates providers on their Privacy Architecture and Interoperability as opposed to exclusively concentrating on features.
• The fit: If you are a regulated enterprise, hardware-backed rooms are your standard; if you are a marketer, ecosystem/network-based DCRs offer the fastest ROI.
• The selection: We compare six leaders: AWS, Databricks, Decentriq, InfoSum, LiveRamp, and Snowflake.

Disclosure: This is a criteria-led comparison based on architectural fit as opposed to a numeric ranking.

What is a data clean room, and how is it different from adjacent platforms?

A data clean room is a secure environment designed for multi-party data collaboration. Its primary function is to allow two or more organizations to join datasets for analysis while ensuring that raw PII (Personally Identifiable Information) remains invisible to all participants.

Crucially, the level of privacy depends on the architecture. While many providers rely on administrative "policies" to restrict their own access, confidential computing-based DCRs use hardware-level enclaves to ensure data is inaccessible even to the DCR provider itself.

It is also important to note that while many marketing platforms now offer clean room features as secondary modules, this guide focuses specifically on purpose-built DCR technology.

• DCRs vs. Data Management Platforms (DMPs): Historically, DMPs were used to combine first- and third-party cookies for audience targeting. In a post-cookie world, DCRs have moved beyond the DMP’s "collect and segment" model to a "collaborate and analyze" model that relies on durable, first-party identifiers.
• DCRs vs. Customer Data Platforms (CDPs): A CDP is your internal system of record for managing customer relationships. A clean room is a neutral computation layer intended for secure data matching with partners who are not in your CDP.
• DCRs vs. Enterprise Data Warehouses (EDWs): A warehouse is where you store and manage your own proprietary data. A clean room acts as a temporary, governed buffer zone where specific slices of that data can meet a partner’s data without either party granting raw access to their full warehouse.
• DCRs vs. Data Exchanges: Traditional exchanges focus on the legal and technical transfer of files between owners. DCRs enable "sharing without sharing", meaning the raw data remains at the source, and only the aggregate insights or model outputs ever leave the environment.

Which data clean room providers should buyers shortlist in 2026?

Shortlisting a provider depends on where your data currently lives, what privacy standards you need to meet, what technical resources you are able to devote to collaborations, and who you intend to collaborate with.

• Decentriq: Best for highly regulated sectors (Banking, Healthcare) requiring hardware-level "zero-trust" security and media and advertising teams looking to safely discover and connect with new collaborators. Also best for any buyer based in DACH.
• Snowflake: Best for organizations already in the Snowflake Data Cloud ecosystem with moderate privacy requirements and a high level of technical proficiency.
• AWS Clean Rooms: Best for high-scale, multi-party SQL analytics within the AWS ecosystem.
• InfoSum: Best for brands whose media budget is managed by WPP since Infosum was acquired by the Holdco. 
• LiveRamp (Habu): Best for marketing teams requiring deep identity resolution and "RampID" integration.
• Databricks: Best for technical teams running complex Machine Learning (ML) and AI workloads via Unity Catalog.

For the purposes of this evaluation, we have excluded any publisher-owned DCR (e.g. Google ADH, Amazon AMC), since they are limited to a single media inventory and therefore don’t offer the type of collaboration covered here.

How should enterprise buyers evaluate data clean room providers?

The success of a data clean room initiative depends on its ability to bypass traditional onboarding bottlenecks while maintaining airtight security and ensuring all legal bases are covered. We recommend evaluating providers against these six core architectural blocks, prioritized by their impact on operational agility.

1. Workflow options: To ensure rapid adoption, a platform must serve both technical and business users. No-code UIs allow non-technical teams to activate collaborations in minutes, alongside Python, R, and SQL notebooks for data scientists who require deep analytical flexibility.
2. Zero-trust framework: Privacy must be enforced by the technology, not just a contractual promise. It is essential to choose a framework that ensures neither your provider nor your collaborating partners can ever access your raw data. Look for hardware-level encryption that guarantees technical isolation between all participants.
3. Scalable commercial model: Avoid rigid, volume-based pricing that penalizes growth. A modern DCR should offer a flexible, value-based commercial model that  accommodates your specific business reality. Critically, it should eliminate the need for invitee partners to enter into their own licensing agreements, allowing for frictionless ecosystem scaling.
4. Partner network & neutrality: Effective collaboration requires platform-agnostic connectivity. Evaluate whether the provider can support multiple cloud connectors (bridging AWS, Azure, and GCP, for example) and integrate seamlessly with key adtech platforms to prevent infrastructural lock-in. Having a network of collaboration-ready partners at your disposal also cuts setup time considerably.
5. Governance & approvals: Control must remain in the hands of the data owner. A robust platform provides granular, multi-party permission workflows, ensuring you maintain full oversight of who sees what and which computations are permitted on your datasets.

Methodology note: This comparison evaluates how the six primary market leaders perform against these six pillars, using technical whitepapers, IAB Tech Lab standards, and release notes from the providers listed.

How do the top data clean room providers compare?

The 2026 comparison matrix

Detailed provider breakdowns

1. Decentriq

As “The Switzerland of Data”, is the pioneer of the neutral clean room model. By using confidential computing, Decentriq ensures that data is encrypted even during the actual computation.

• Best fit: Highly regulated industries (Finance, healthcare, public sector), or organizations in the advertising industry who need to comply with EU data protection policies (especially in the DACH region) and get started collaborating fast.
• Key strength: Ease of use and a network of ready-to-collaborate organizations already onboarded into the Decentriq ecosystem.
• Key trade-off: Requires data to be sent into the enclave for secure processing.
• Notable privacy point: Offers cryptographic attestation, giving legal teams a verifiable proof that privacy policies were actually enforced by the hardware.

2. Snowflake Data Clean Rooms

Snowflake has leveraged its acquisition of Samooha to offer a no-code UI on top of its "Global Data Clean Room" framework.

• Best fit: Enterprises with their data already in Snowflake.
• Key strength: Zero-copy sharing. No data leaves the Snowflake environment (but must also be found there to begin with).
• Key trade-off: High cloud lock-in. Collaborating with non-Snowflake partners is possible but adds technical friction.
• Notable privacy point: Relies on Snowflake Horizon Catalog: a powerful suite, but  dependent on Snowflake’s platform and infrastructure.

3. AWS Clean Rooms

AWS has focused on scale, allowing up to 10 parties to join a single collaboration with minimal latency.

• Best fit: AWS-heavy organizations.
• Key strength: Scalability and SQL flexibility.
• Key trade-off: Complex setup. Managing roles requires dedicated engineering resources.
• Notable privacy point: Multi-layered privacy stack relying on several types of privacy-enhancing technologies (PETs).

4. InfoSum

Since its 2025 acquisition by WPP, InfoSum has transitioned from an independent startup to the foundational data layer for the GroupM/WPP ecosystem. By integrating its "non-movement" technology directly into the WPP Open operating system, it has become the primary engine for WPP clients looking to train custom AI models without exposing raw first-party data.

• Best fit: Media owners and brands with high security requirements.
• Key strength: Decentralized matching. No "central bunker" is created.
• Key trade-off: Less flexible for free-form data science use cases.
• Notable privacy point: Patented synthetic ID process to eschew data movement.

5. LiveRamp (Habu)

The LiveRamp Data Collaboration Platform (powered by Habu) is a popular choice for organizations requiring a pre-built activation footprint. 

• Best fit: Brands focusing on cross-screen measurement and attribution.
• Key strength: RampID: The ability to resolve identities across the fragmented web is built directly into the room.
• Key trade-off: Expensive solution, difficult to scale commercially.
• Notable privacy point: Offers “Quick-Start Media Templates” that are pre-vetted for compliance.

6. Databricks Clean Rooms

Built on top of Unity Catalog, Databricks Data Clean Rooms is the primary choice for data engineering-heavy organizations already standardized on its Lakehouse architecture.

• Best fit: Data scientists and engineers building complex AI models on shared datasets.
• Key strength (Notebook-native): It provides a code-first environment that goes beyond SQL, allowing technical teams to use Python, R, and Spark within collaborative notebooks.
• Key trade-off: It has a steeper learning curve for non-technical users; every participant must have Unity Catalog and Delta Sharing enabled in their own workspace before a clean room can even be created.
• Notable privacy point: The platform relies on permissions-based access control and has a maximum limit for participating organizations per collaboration.

What privacy and operating-model differences matter most in practice?

The biggest mistake buyers make in 2026 is assuming all clean rooms are more or less the same. Instead, it’s helpful to sort them into two primary models:

1. Cloud platform-managed models (AWS, Databricks, Snowflake,): These are integrated rooms. They rely on software-based policy rules. You trust the cloud provider to enforce the rules. The technologies are highly efficient but lack technical neutrality, and often require data science skills from their users.
2. Cloud-independent models (Decentriq, InfoSum, LiveRamp): These are neutral spaces for data collaboration. Decentriq, for example, uses hardware-backed confidential computing to ensure that even the room's administrator cannot see the data. This is a higher trustless bar required for regulated data. Furthermore, they are traditionally built for use from non-business users as well. Increasingly, such data clean rooms are absorbed into agencies or other tech platforms. Decentriq remains under independent ownership.

Which provider fits your operating model?

• Publisher / media owner: Your goal is to prove audience value without leaking your subscriber list. InfoSum, LiveRamp, or Decentriq are the strongest fits due to their networks and no-code interfaces.
• Single data owners needing to provide insights to large numbers of collaborators (one-way collaboration): You need to enable 50+ brand partners to query your sales data. AWS or Snowflake offer the scalability and template approach needed to manage many-to-one relationships.
• Regulated enterprise (bank/insurance/pharma): You are legally responsible for PII even in a clean room. Decentriq is the priority here because its hardware-backed security satisfies the most stringent technical and organizational measures.
• Multi-party consortium: If 5+ companies are joining data (e.g., an anti-fraud network), AWS or Decentriq are best equipped to handle multi-party joins without performance degradation.

FAQs about choosing a data clean room provider

Are data clean rooms GDPR-compliant by default?

No. While a DCR is a powerful architectural framework for data protection by design, it does not grant automatic compliance.

A DCR works by orchestrating a mixture of privacy-enhancing technologies (PETs). However, even with these technical safeguards, you must still satisfy the legal requirements of the GDPR:

• Valid legal basis (Article 6): The PETs inside a DCR handle how the data is processed, but they do not provide the why. You still require a documented legal basis (typically Explicit Consent or a Legitimate Interest Assessment) before any data enters the room.
• Purpose limitation: Using a DCR does not allow you to bypass the original reason the data was collected. If a user consented to account management, you cannot move that data into a marketing attribution clean room without verifying that the new use case is compatible with the original purpose.
• Data minimization: Under Article 25, you are obligated to process only the data strictly necessary for the specific task. A DCR makes this easier to enforce, but the responsibility for selecting only the necessary data points remains with the data controller.

Can a clean room work without moving raw data?

Yes. There are different technological means to achieve this. Historically, organizations have used hashing algorithms to avoid moving raw data, thus only moving pseudonymized data. However, hashing techniques can be prone to data leaks and user re-identification. Trusted Execution Environments have now become the new standard for processing data in cleanrooms, only encrypted copies of the raw data are moved to the clean room and no one can ever access the raw data.

What audit evidence should legal and security teams ask for?

Always ask for cryptographic attestation (from enclave providers) or differential privacy logs to prove that no re-identification occurred.

Which providers support more than two parties?

Most enterprise clean rooms have moved beyond the "1-to-1" model, but they handle multi-party complexity in different ways:

• AWS Clean Rooms: Historically limited, it now officially supports up to 5 participants per single collaboration. While you can have many data contributors, the "logical boundary" of a single room is capped to ensure query performance across their decentralized S3 architecture.
• Databricks Clean Rooms: Each clean room is limited to a total of 10 collaborators (1 creator plus up to 9 invited partners). This is a hard limit tied to their Unity Catalog and Delta Sharing governance model.
• Decentriq: Because it uses confidential computing, Decentriq supports multi-party enclaves without a fixed technical cap on participants. This is because the hardware enclaves isolate the computation entirely, making it the standard for large-scale industry consortia (e.g., banking or healthcare networks with 20+ members).
• InfoSum: Rather than a limit, InfoSum uses a network approach. Multiple partners (e.g., a brand, three retailers, and two media owners) can all connect their separate Bunkers simultaneously to create a unified view, referred to as a private data network.
• Snowflake: Recently introduced multi-provider support (currently in public preview/GA transition for 2026), allowing a single consumer to run an analysis across multiple data providers in one go.

In summary: If you are planning a simple 1-to-1 partnership (e.g., one brand + one retailer), any of these will work. However, if you are building an ecosystem (e.g., a Brand matching against 15 different publishers at once), you should choose a provider that doesn't force you to build 15 separate rooms.

When is a clean room the wrong tool?

A DCR is an effort multiplier. So if the foundations are weak, it will only multiply friction. It is likely the wrong choice if:

• The data is already public or low-risk: If you are analyzing non-sensitive weather patterns, public stock prices, or anonymized census data, the specialized capabilities of a DCR add unnecessary compute costs and latency without providing any security benefit.
• You have full data rights: If you are joining two datasets that you already own (e.g., merging data from a recent acquisition), a standard data warehouse or CDP is faster and more flexible. You don't need a neutral room when you are the only party involved.
• Your data quality is poor: A DCR does not clean your data's quality; it only maintains its privacy. If your customer records are full of duplicates or inconsistent formats, the match rates in the clean room will be too low to provide any statistical value.

How we evaluated providers

Our 2026 evaluation is based on technical audits, the IAB Tech Lab Data Clean Room Guidance v2.0, and direct vendor release notes. We focus on architectural proof rather than self-reported feature lists. 

Disclosure: Decentriq is the publisher of this guide and is included as the primary specialist for confidential computing-based clean rooms.

Summary for buyers:

• The best room: Is the one that bridges your data to your partner's without compromising your security posture.
• Fit cue: If your legal team says no, look at Decentriq's hardware-backed model.
• Next step: See how confidential computing changes the trust equation. Book a demo today.