Samooha
Data clean room software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
- Market presence
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What is Samooha
Samooha is a data clean room solution that enables organizations to collaborate on data analysis and audience activation without directly sharing raw, row-level data. It is used by data, analytics, and marketing teams to run privacy-preserving queries and measurement workflows across first-party datasets. The product is designed to operate with governed access controls and policy enforcement, typically in conjunction with a cloud data platform environment. It emphasizes standardized collaboration workflows (e.g., audience overlap, measurement) and controlled outputs to reduce data leakage risk.
Privacy-preserving data collaboration
Samooha supports collaboration patterns where parties analyze combined datasets while limiting exposure of underlying records. It typically relies on policy controls and constrained outputs (such as aggregated results) to reduce the risk of re-identification. This aligns with common clean room requirements for regulated industries and privacy-focused marketing measurement. It is well-suited for scenarios where partners need insights without exchanging raw data extracts.
Governance and access controls
The product is oriented around governed collaboration, including role-based access and rules around what queries and outputs are permitted. These controls help organizations operationalize internal privacy and security requirements when working with external partners. Compared with ad-platform-specific clean rooms, this approach can be applied to a broader set of collaboration use cases. It also supports repeatable workflows rather than one-off data sharing.
Workflow-based clean room use cases
Samooha focuses on common clean room workflows such as audience overlap analysis, measurement, and activation-oriented outputs. Standardized templates and repeatable processes can reduce the effort required to stand up partner collaborations. This can be beneficial for teams that need to scale collaborations across multiple partners with consistent rules. It also helps align technical execution with business-facing use cases.
Ecosystem dependence and fit
Clean room deployments often depend on the underlying data platform, identity approach, and partner connectivity available in an organization’s stack. If Samooha is tightly coupled to a specific cloud data environment or set of integrations, organizations outside that ecosystem may face additional implementation work. Partner participation can also be constrained by what platforms and connectors are supported. This can limit time-to-value for cross-ecosystem collaborations.
Output constraints limit flexibility
By design, clean rooms restrict outputs to reduce privacy risk, which can limit exploratory analytics and certain modeling approaches. Teams may need to redesign analyses to fit allowed query patterns and aggregation thresholds. This can be a friction point for advanced data science use cases that require granular feature engineering. It may also require stakeholder education to set expectations on what can and cannot be produced.
Implementation and governance overhead
Operationalizing a clean room typically requires data preparation, policy definition, and ongoing governance processes. Organizations may need dedicated resources to manage permissions, partner onboarding, and compliance reviews. Without clear internal ownership, deployments can stall or become underutilized. The total effort can be higher than using a single-purpose measurement environment embedded in an advertising platform.
Plan & Pricing
Pricing model: Pay-as-you-go Free tier/trial: Not explicitly published for Samooha as a standalone product on vendor sites. Snowflake Marketplace listings can offer limited trials or free listings (trial types are configurable by the provider); Snowflake also offers trial accounts for evaluating Snowflake features. See notes. Example costs / charge components:
- Warehouse / compute: standard Snowflake warehouse credit consumption billed to the account running queries inside a clean room.
- Serverless tasks: costs for serverless tasks (e.g., scheduled/statistics tasks) that support clean room operations.
- Provider background processes: providers incur credit consumption for background procedures required by clean rooms (data registration, clean room creation/installation/editing, Identity Hub calls, daily statistics tasks). These are billed as Snowflake usage/credits.
- Marketplace listing charges: If a provider monetizes a Snowflake Native App or Marketplace listing (e.g., Samooha as a Native App), pricing can be configured as usage-based (per-query, per-event, monthly fee) or subscription-based; providers set the price and optional trials for listings.
Discount options: Capacity commitments and enterprise/volume discounts are handled at the Snowflake contract level (consumers can use Capacity commitments to purchase Marketplace listings).
Notes & clarifications:
- No public, fixed subscription prices for “Samooha” (data clean room) were found on vendor official sites; costs are described as consumption-based (Snowflake credits) rather than a fixed per-seat/month price.
- Samooha was acquired and integrated into Snowflake; Samooha functionality is delivered as Snowflake Data Clean Rooms / a Snowflake Native App (Snowflake Marketplace).
Seller details
Snowflake Inc.
Bozeman, Montana, USA
2012
Public
https://www.snowflake.com/
https://x.com/SnowflakeDB
https://www.linkedin.com/company/snowflake-computing/
