AWS Clean Rooms vs Snowflake vs LiveRamp: clean room pricing, ML and APIs compared

Every cell is sourced; "n/d" means the vendor publishes nothing on the point at the research date shown above.

AWS is the easiest of the three to cost, because the units are on the page. Queries are charged in Clean Rooms Processing Units per hour, billed per second with a 60-second minimum. PySpark jobs carry a 10-minute minimum. By default, AWS gives each Spark SQL query 32 CRPUs (AWS pricing).

AWS's own first example shows the arithmetic (AWS pricing). A 3-minute query run three times a day uses 0.15 hours. At 32 CRPUs and $2.00 per CRPU-hour, that is $9.60 a day, or $3,504 a year. Turn on differential privacy and the rate doubles to $4.00 per CRPU-hour in AWS's third example.

The other meters matter as soon as you go beyond SQL (AWS pricing). A lookalike segment costs $0.25 per 1,000 profiles, so a single 2-million-profile segment costs $500. Rule-based record matching costs $0.50 per 1,000 records matched plus a one-time $100 per collaboration. Members also pay normal S3 and Glue request fees on the data they contribute (same source).

Snowflake does not sell the clean room as a line item. When the app reached general availability in March 2024, InfoWorld reported it was free in the Snowflake Marketplace (InfoWorld, 28 March 2024). Today's documentation describes the cost in terms of operations (Snowflake docs):

1. User-initiated workloads, such as analysis and activation queries, run on the warehouse the user picks and consume credits.
2. Management actions, such as registering data, creating or joining a room, also run as stored procedures on a warehouse.
3. Ongoing operations, such as upgrades and auto-join, run as serverless tasks.
4. Orchestration across collaborators is "currently" not charged back, though Snowflake says it "may" be in future.

The credit price itself depends on edition, cloud and region, and on whether you buy on demand or pre-paid capacity (Snowflake pricing options). Edition is a hidden cost driver. A company that provides data into a room needs Enterprise Edition; owners and runners can stay on Standard (Snowflake docs).

LiveRamp moved from fixed-fee subscriptions to a consumption model "based directly on customer feedback" (LiveRamp pricing). Customers commit to a yearly block of tokens. Products draw tokens down at rates that improve with larger commitments, and overage tokens can be bought.

LiveRamp gives three tiers with sizing guides (LiveRamp pricing). Standard starts at $20,000 a year and suits fewer than 5 million customer records and 1 to 3 media destinations. Premier covers 5 to 25 million records and 4 to 10 destinations. Enterprise covers more than 25 million records and over 10 destinations. Prices for the upper tiers are not published.

Two cautions apply. The tiers price the LiveRamp platform as a whole, so the clean room share of a contract is not visible. And ownership changed on 2 November 2026, when Publicis Groupe completed the acquisition under an FTC consent order that requires commercial neutrality (PPC Land, 2 November 2026).

Take one brand and one retailer. They run a 3-minute audience overlap query three times a day for a year and build no models, which matches AWS's first published example (AWS pricing). Here is what each vendor's public material lets you calculate:

The figures are not like for like. The AWS number is pure compute. The LiveRamp number buys platform access that includes identity and activation. The Snowflake number depends on a rate only your contract shows. A fair comparison needs your own volumes run through each vendor's quote.

AWS has the deepest packaged ML. Clean Rooms ML trains an AWS-built lookalike audience model and also runs custom models that partners bring. Since 30 November 2025 it can generate synthetic training data (AWS document history). Custom models are priced per million records, plus the chosen ML instance and the SQL that prepares the data.

Snowflake caught up in 2026. ML Jobs run full Python ML workloads with distributed training, custom packages and GPU compute in isolated containers (Snowflake, 8 July 2026). Snowflake also documents lookalike modelling and multi-party incrementality templates built on ML Jobs (Snowflake docs, ML Jobs).

LiveRamp's route is general-purpose. Clean Compute on Apache Spark lets an owner run custom Python against its own and partner data in hybrid and confidential computing rooms (LiveRamp docs). It suits teams with their own data scientists more than teams that want a ready-made lookalike model.

Joining two customer files is the first step in any collaboration, and the three vendors take different routes. AWS sells entity resolution inside the collaboration. Members can match on their own rules, or use a data provider's IDs, such as LiveRamp RampIDs, after buying that provider's subscription (AWS pricing). Provider-based matching runs only in three US Regions.

Snowflake stays neutral and lets owners plug in identity connectors; its documentation describes a TransUnion TruAudience connector (Snowflake docs, connectors). LiveRamp is the identity vendor. RampID is built in, and its June 2026 release made the question builder check PII, RampID and known-ID datasets before a query runs (LiveRamp release notes).

The practical upshot: if you already license RampID, LiveRamp or AWS with RampID matching both work. If you do not want any identity vendor in the loop, Snowflake's design makes that the default.

All three offer the two standard tools. Each applies them differently.

Minimum group sizes. AWS enforces a per-row minimum of distinct users set by each data owner, and applies the strictest owner's number when tables meet (AWS user guide). LiveRamp has two layers: a crowd size on output measures, set by the question author, and an aggregation threshold rule on input data, which LiveRamp validates before a partner's question can run (LiveRamp docs). Snowflake leaves thresholds to the owner's templates and settings. These floors work like k-anonymity.

Noise. AWS offers managed differential privacy with a privacy budget and a set amount of noise per query; it costs extra compute. Snowflake's version is off by default, picks entity columns itself and refreshes each account's budget daily (Snowflake docs). LiveRamp offers Laplacian noise injection.

Beyond statistics. AWS can encrypt columns client-side with C3R (AWS user guide, C3R). LiveRamp can run rooms inside a trusted execution environment on Azure (LiveRamp docs). Snowflake relies on per-owner data offering specs and policies.

• Both parties already on AWS, and cost must be modelled up front: AWS Clean Rooms. The published units make budgeting possible before procurement.
• Both parties on Snowflake and wary of identity vendors: Snowflake Data Clean Rooms. Data offerings are live views and nothing is copied.
• Many partners on different clouds, with people-based matching: LiveRamp Clean Room. Hybrid rooms span clouds and RampID handles the join.
• A partner on Databricks: AWS can now read Databricks tables through Delta Sharing; also see Databricks Clean Rooms.
• Measurement of Amazon media specifically: neither of the three replaces Amazon Marketing Cloud, which runs on AWS Clean Rooms.

Whether a clean room or a customer data platform should sit at the centre is a separate decision, covered in clean rooms vs CDP for the enterprise. Each vendor also has a full profile: AWS Clean Rooms, Snowflake Data Clean Rooms and LiveRamp Clean Room.

1. AWS, "AWS Clean Rooms pricing" (accessed 9 November 2026). https://aws.amazon.com/clean-rooms/pricing/
2. AWS, "Document history for the AWS Clean Rooms User Guide" (accessed 9 November 2026). https://docs.aws.amazon.com/clean-rooms/latest/userguide/doc-history.html
3. AWS, "Analysis rules in AWS Clean Rooms", user guide (accessed 9 November 2026). https://docs.aws.amazon.com/clean-rooms/latest/userguide/analysis-rules.html
4. AWS, "AWS Clean Rooms ML", user guide (accessed 9 November 2026). https://docs.aws.amazon.com/clean-rooms/latest/userguide/machine-learning.html
5. AWS, "AWS Clean Rooms Differential Privacy is now generally available" (10 April 2024). https://aws.amazon.com/about-aws/whats-new/2024/04/aws-clean-room-differential-privacy
6. AWS, "AWS Clean Rooms API Reference" (accessed 9 November 2026). https://docs.aws.amazon.com/clean-rooms/latest/apireference/Welcome.html
7. AWS, "AWS Clean Rooms customers" (accessed 9 November 2026). https://aws.amazon.com/clean-rooms/customers/
8. AWS, "Cryptographic Computing for Clean Rooms", user guide (accessed 9 November 2026). https://docs.aws.amazon.com/clean-rooms/latest/userguide/crypto-computing.html
9. AWS, "Creating a collaboration for queries", user guide (accessed 9 November 2026). https://docs.aws.amazon.com/clean-rooms/latest/userguide/create-collab-queries.html
10. Snowflake documentation, "Overview of Snowflake Data Clean Rooms" (accessed 9 November 2026). https://docs.snowflake.com/en/user-guide/cleanrooms/overview