European data protection authorities issued €1.2 billion in GDPR fines in 2025 and now receive 443 data breach notifications per day, a 22% jump year-on-year, according to DLA Piper's 2026 GDPR Fines and Data Breach Survey.

The same pressure is building under HIPAA, CCPA, and sector-specific frameworks globally, meaning the question of who can access your data has moved from legal footnote to procurement criterion.

If you're evaluating LiveRamp, the right first question is which part you're replacing. This article covers only the clean room and data collaboration layer. We present six alternatives, evaluated by privacy architecture, identity approach, and regulatory readiness.

Best LiveRamp alternatives at a glance

Here's how the six platforms compare across neutrality, privacy model, identity approach, and regulatory readiness. 

What are you actually replacing?

LiveRamp covers several overlapping product areas. The most distinct is identity resolution: RampID and IdentityLink, the infrastructure that powers matching and activation across its 900 partners. If your evaluation centers on the identity layer (match rates, RampID's US-centric ecosystem, onboarding cost) the relevant alternatives are Neustar/TransUnion, Experian, and Epsilon. None of the platforms in this article replace that.

What this article covers is the clean room and data collaboration layer, now operating under the LiveRamp Clean Room name following the 2023 acquisition of clean room specialist Habu. LiveRamp's clean room is policy-based: privacy is governed by access controls and contractual constraints rather than hardware enforcement. That's the baseline everything below is being compared against.

This article evaluates independent and platform-agnostic clean room solutions, along with data platform providers that offer clean room capabilities as a significant part of their offering. It does not cover hyperscaler-native or walled-garden products such as Google Ads Data Hub, Amazon Marketing Cloud, or AWS Clean Rooms, which are tied to a single media inventory or cloud ecosystem and warrant a separate evaluation.

6 best LiveRamp competitors & alternatives compared

Each entry covers four aspects: privacy architecture, identity approach and ecosystem dependency, regulatory readiness, and deployment overhead.

Privacy architecture is where the sharpest divisions sit: policy-based platforms vary in ecosystem, pricing, and deployment model, but they share the same fundamental trust assumption: that the platform operator acts within its agreed constraints. Confidential computing removes that assumption entirely.

Decentriq - Enterprises and media companies that need verifiable privacy and fast time-to-collaboration

Decentriq is a Swiss data clean room platform built on confidential computing. The fundamental difference from every other platform here: rather than relying on policy-based access controls, Decentriq processes all computations inside hardware-isolated enclaves where data stays encrypted even during analysis.

The result is a clean room where neither Decentriq nor the cloud provider can access underlying data, not by policy, but by cryptographic proof. As a result, customers can bypass lengthy, complicated legal agreements needed for each collaborating partner and instead get started collaborating (and scaling their collaborations) quickly.

Standout features

• Cryptographic attestation: any party can independently verify the clean room is running the expected code in a genuine hardware enclave before submitting data, which is technically impossible to replicate with a policy-based model
• Collaborative Audience Platform: enables audience creation and activation workflows, reducing dependency on external identity infrastructure 
• No-code and SQL interfaces: accessible beyond data science teams without sacrificing analytical depth
• Production-deployed globally: deployments across multiple jurisdictions, upholding data protection regulations in each one
• Established partner network: a growing ecosystem of publishers, retailers, and data providers already onboarded into the platform, reducing time-to-collaboration for new customers (particularly strong in the DACH region)

Limitations

• Computational overhead: confidential computing is slightly slower than in-memory warehouse queries; evaluate latency for high-volume use cases
• Smaller partner ecosystem: fewer pre-built integrations than LiveRamp's network

Best fit

Decentriq is the right choice for media companies globally and any organization requiring verifiable rather than contractual privacy guarantees. If your compliance or legal team needs proof that the clean room operator cannot access your data, Decentriq is the only platform here that provides it at the hardware level.

Our Decentriq vs LiveRamp comparison covers the architectural differences in depth.

InfoSum - Best for: Industries needing strong guarantees that data won’t be moved

InfoSum is a UK-headquartered clean room built on a decentralized, non-movement architecture: each contributor's data sits in an isolated cloud instance that never moves for analysis or activation. When two parties collaborate, aggregate outputs are generated without the underlying data being transferred or commingled.

In April 2025, InfoSum was acquired by WPP and joined GroupM. The platform continues to work with non-WPP partners, but if commercial neutrality and independence matters for your evaluation, the ownership context is worth factoring in.

Standout features

• Patented non-movement technology: data is analyzed in place, never transferred between parties
• Drag-and-drop audience builder: makes clean room functionalities accessible to marketers, not just data scientists

Limitations

• Policy-based model: guarantees rest on contractual controls and differential privacy, not hardware enforcement
• Narrow ecosystem: strongest in UK and European media; less established in sectors beyond advertising
• WPP ownership: introduces commercial dependency; advanced ML workloads are not a primary use case
• Proprietary query language: InfoSum uses its own query language (IQL) rather than SQL or Python, creating a learning curve and limiting analytical flexibility beyond predefined collaboration patterns, so analysts cannot apply existing skills directly 

Best fit

InfoSum suits UK and European media, broadcasting, and retail organizations that need GDPR-aligned architecture and place value on a non-movement data model. Organizations that require hardware-level privacy guarantees should evaluate Decentriq instead. Buyers for whom vendor independence is a procurement criterion should factor in the WPP ownership.

Snowflake Data Clean Rooms - Best for: Enterprise teams already running Snowflake infrastructure

Snowflake Data Clean Rooms are built directly into the Snowflake Data Cloud, leveraging its 2023 acquisition of Samooha to add a no-code interface on top of its native clean room framework. Each party's data stays in their own account; a shared, governed workspace lets both sides run joint analysis without exchanging raw data. Access policies control which queries each collaborator can run, and results only come back once they clear the aggregation thresholds you've set.

Snowflake supports deployment across AWS, Azure, and GCP. However, the clean room only works with data that already lives in Snowflake, meaning collaboration with non-Snowflake partners is possible but adds meaningful technical friction. LiveRamp's Data Collaboration Platform also integrates with Snowflake, allowing teams to combine LiveRamp's identity layer with Snowflake's governance infrastructure.

Standout features

• Zero additional infrastructure: clean room capability activates within the existing Snowflake environment; nothing extra to provision or maintain
• SQL-native interface: supports complex queries and custom analytics at warehouse scale
• Snowflake Marketplace and Data Exchange: adds partner discovery on top of the clean room layer

Limitations

• Snowflake-only architecture: the clean room only functions with data already in Snowflake
• Cross-cloud friction: collaborating with non-Snowflake partners requires additional engineering work
• Limited non-technical user access: despite Samooha's no-code layer, Snowflake's clean room remains primarily SQL-native and requires significant technical expertise to configure and operate
• Policy-based governance: privacy controls rely on Snowflake's own governance layer rather than hardware enforcement, meaning trust in the platform operator is a prerequisite

Best fit

Snowflake Data Clean Rooms suit enterprise teams that have standardized on Snowflake and want clean room capabilities without adding another vendor. The hard data residency requirement makes them a poor fit for multi-cloud or cloud-agnostic environments, and the policy-based privacy model rules them out for regulated use cases where hardware-backed guarantees are a compliance requirement. For a direct comparison of how Snowflake's clean room architecture stacks up against Decentriq's, our Decentriq vs Snowflake comparison covers the key differences.

Databricks Clean Rooms - Best for: Data science and ML teams running complex analytics workloads

Databricks Clean Rooms are built on Delta Sharing, Databricks' open protocol for governed data exchange. Where most clean rooms are built around audience matching and campaign measurement, Databricks is oriented toward technically heavier work: joint model training, advanced analytics pipelines, and anything that needs notebook-level flexibility rather than a query-restriction UI.

Data remains in each contributor's Databricks environment; Delta Sharing governs what the receiving party can access and under what conditions. External platforms like Snowflake and BigQuery can also be brought in via Lakehouse Federation without copying data into Databricks, which reduces infrastructure lock-in for organizations already running mixed environments.

Standout features

• Notebook-level analytics: native Python, Scala, SQL, R, and Java support gives data science teams depth beyond pure SQL clean rooms
• Cross-cloud collaboration: participants on different clouds — AWS, Azure, or GCP — can collaborate in the same clean room provided each has Unity Catalog and Delta Sharing enabled
• Open Delta Sharing protocol: collaboration partners can connect data from external platforms including Snowflake and BigQuery without full migration to Databricks

Limitations

• Unity Catalog prerequisite: every participant must have Unity Catalog enabled and Delta Sharing configured in their own workspace before a clean room can be created
• 10-participant cap: each clean room supports a maximum of 10 organizations, which constrains consortium-style use cases
• Technical users only: marketing teams without data science support will find the tooling inaccessible
• Policy-based governance: no hardware enforcement
• Bilateral infrastructure requirement: unlike most platforms here, every collaboration partner must also have Unity Catalog and Delta Sharing configured in their own Databricks environment before a clean room can be created

Best fit

Databricks suits data and engineering teams already running on the Databricks platform who need clean room capabilities for ML-heavy or analytically complex workloads. The 10-participant cap is worth factoring in for any multi-party or consortium use case. It's not the right choice for marketing-led use cases, non-technical teams, or organizations prioritising hardware-backed privacy.

For a detailed comparison of the architectural differences with Decentriq's confidential computing approach, our Decentriq vs Databricks comparison goes into depth.

Optable - Best for: Publishers and broadcasters building cookieless audience infrastructure

Optable is built for publishers and media owners. According to a joint Digiday and Optable report from 2025, 78% of publishers are building or operating an identity graph. Where most platforms treat publishers as one half of a collaboration, Optable treats the publisher as the primary customer: the platform is designed to help them build and monetize first-party identity graphs, then collaborate with advertisers through a clean room layer.

The platform uses multi-party computation, including Private Set Intersection (PSI), to match audiences without raw data exposure.

Standout features

• Integrated publisher stack: identity graph creation, audience activation, and clean room collaboration in a single platform
• Flash Node architecture: allows customers to invite partners to collaborate securely around audiences at no cost to the invited partner, meaning collaborators don't need to be Optable customers to participate in a clean room
• Limited analytical flexibility: designed for predefined audience collaboration patterns rather than complex or custom analytics workloads 

Limitations

• North American roots: international partner network and case study base is less developed than North American equivalents
• Policy-based model: hardware enforcement is not part of the architecture

Best fit

Optable is the strongest fit for publishers, broadcasters, and media networks building cookieless audience infrastructure and monetizing first-party data through direct advertiser relationships. Organizations in regulated industries and/or which have strict data sovereignty requirements should evaluate Decentriq instead.

Note: Optable's primary product focus is its audience platform and publisher identity graph. The clean room layer is one component of a broader stack rather than a standalone offering — buyers evaluating Optable specifically as a clean room solution should verify that the current product scope meets their requirements before proceeding. 

How to choose a LiveRamp alternative

Three questions will narrow the field faster than any feature comparison:

1. What privacy architecture does your use case require? Policy-based clean rooms (InfoSum, Snowflake, Databricks, Optable) are the right fit for straightforward advertising and media use cases where setup speed is not a priority. Confidential computing takes a different approach: because privacy is enforced at the hardware level rather than through governance configuration, there is less complexity to manage, which translates into faster deployment and easier scaling across partners. For organizations in healthcare, financial services, pharma, or public sector, hardware-enforced privacy is often a compliance requirement. However, the scalability advantage applies equally to media and advertising buyers who need to move quickly. Decentriq is the only platform here built on that foundation.
2. What is your infrastructure and where does your data live? If you're on Snowflake or Databricks, the native clean room product is the obvious starting point. If you're cloud-agnostic or multi-cloud, InfoSum and Decentriq remove the cloud dependency. For a wider view of the data collaboration landscape, our guide to the best data collaboration platforms in 2026 covers the category.
3. What are your regulatory requirements and geographic priorities? LiveRamp's RampID is US-centric; its clean room operates globally but the partner network skews heavily toward North America. In any regulated market where data sovereignty and operator neutrality are real requirements (GDPR, HIPAA, or sector-specific frameworks in Asia-Pacific and the Middle East), policy-based models can't provide the same verifiable guarantee. Decentriq's architecture and independent ownership satisfies that requirement regardless of jurisdiction.

For a deeper comparison of clean room privacy architectures across the full market, our guide to the best data clean rooms in 2026 goes deeper.

Frequently asked questions

How does Decentriq compare to LiveRamp for data collaboration?

Three differences stand out. First, neutrality: LiveRamp owns RampID, a proprietary identity solution, which means it has a commercial interest in how clean room workflows are structured. Decentriq has no proprietary identity graph; while its Collaborative Audience Platform supports audience creation and activation, it works with your existing identity infrastructure rather than replacing it with a Decentriq-owned alternative. 

Second, cost: LiveRamp is more expensive. 

Third, privacy architecture: LiveRamp's clean room is policy-based, meaning privacy controls operate at the governance layer. Decentriq uses confidential computing, where data is processed inside hardware-isolated enclaves and neither Decentriq nor the cloud provider can access it. That guarantee is cryptographic and independently verifiable, rather than contractual and configurable. For regulated use cases — or any organization that needs to onboard partners quickly without lengthy legal negotiation — that distinction matters. 

Which platforms here are best suited to European markets?

European buyers face two distinct considerations: GDPR compliance at the infrastructure level, and operator-level neutrality (the question of whether the platform provider itself can access your data). Most platforms in this article are GDPR-compliant by configuration, but infrastructure compliance and operator neutrality are not the same thing.

InfoSum's non-movement architecture and UK headquarters make it aligned with GDPR principles. Decentriq's confidential computing model goes further: because privacy is enforced at the hardware level, neither Decentriq nor the cloud provider can access underlying data: a guarantee that holds regardless of jurisdiction. For European buyers where data sovereignty is a hard requirement rather than a configuration preference, that distinction is decisive because it means Decentriq was designed for GDPR compliance from the ground up.

What do I lose at the clean room layer by switching away from LiveRamp?

The primary consideration is partner familiarity rather than technical capability. Organizations that have already built workflows around LiveRamp's clean room interface will face some retraining and reconfiguration. Beyond that, the practical impact depends on your use case: if you're collaborating with known partners, the transition is largely technical. 

If your workflows depend on LiveRamp-specific integrations or reporting formats, those will need to be rebuilt. What you don't necessarily lose is capability — several platforms here match or exceed LiveRamp's clean room functionality, and some, like Decentriq, offer privacy guarantees that LiveRamp's policy-based model cannot provide. 

Architecture is the decision

LiveRamp covers several overlapping product areas: identity resolution, clean room collaboration, activation, and measurement. Which platform fits depends on architecture, not brand.

Policy-based clean rooms cover most advertising and media use cases and are a natural starting point for teams already embedded in Snowflake or Databricks. But there is a tradeoff that doesn't always surface in vendor comparisons: policy-based models require legal agreements to be negotiated with each new partner before collaboration can begin. Confidential computing removes that dependency, because the trust question is answered cryptographically rather than contractually, which significantly reduces the legal overhead of onboarding new partners and accelerates time-to-collaboration.

For buyers in healthcare, financial services, pharma, or public sector, hardware-enforced privacy is often a compliance requirement on top of that. But the onboarding speed advantage applies equally to media and advertising organizations that need to scale partner relationships quickly.

Decentriq is the only platform here built on that foundation. If you want to see how that works in practice for your use case, book a demo.