Amazon Clean Rooms
Amazon Clean Rooms enables organizations to collaborate and analyze shared datasets without exposing underlying raw data to partners. Create secure data clean rooms in minutes and collaborate with any company while maintaining data privacy through differential privacy, cryptographic computing, and flexible analytics.
Solid contracts, transparent operations, and an easy start — typically complete on four or five facets with one clear soft spot.
API Evangelist profiles Amazon Clean Rooms the way a machine reads it — 81 machine-readable artifacts across 4 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Amazon Clean Rooms scores 62.0/100 (strong), with a separate agent-readiness read of 48/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Amazon Clean Rooms
Each block below is one kind of artifact we hold for Amazon Clean Rooms. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 4
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Amazon Clean Rooms Collaborations API
Operations for managing Clean Rooms collaborations
Amazon Clean Rooms Configured Tables API
Operations for managing configured tables
Amazon Clean Rooms Memberships API
Operations for managing collaboration memberships
Amazon Clean Rooms Protected Queries API
Operations for executing and managing protected queries
Features 6
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Privacy-Preserving Analytics
Analyze shared datasets without exposing underlying raw data using differential privacy and cryptographic computing.
Zero-ETL Integration
Collaborate with Snowflake and AWS datasets without data movement or ETL pipelines.
Protected Queries
Execute SQL, PySpark, or ML model queries on partner data with configurable privacy controls.
Flexible Analysis Methods
Run analytics using SQL, PySpark, or custom ML models with granular access controls.
Analysis Logging
Audit data usage with analysis logging to track all queries run within a collaboration.
Customer Record Matching
Match customer records across applications and channels without sharing PII.
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Amazon Clean Rooms Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Amazon Clean Rooms API Rules
SPECTRALAmazon Clean Rooms API Rules
SPECTRALJSON Schema 17
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
Collaboration
JSON SCHEMAConfiguredTable
JSON SCHEMACreateCollaborationRequest
JSON SCHEMACreateCollaborationResponse
JSON SCHEMACreateConfiguredTableRequest
JSON SCHEMACreateConfiguredTableResponse
JSON SCHEMACreateMembershipRequest
JSON SCHEMACreateMembershipResponse
JSON SCHEMAGetCollaborationResponse
JSON SCHEMAListCollaborationsResponse
JSON SCHEMAListConfiguredTablesResponse
JSON SCHEMAListMembershipsResponse
JSON SCHEMAListProtectedQueriesResponse
JSON SCHEMAMembership
JSON SCHEMAProtectedQuery
JSON SCHEMAStartProtectedQueryRequest
JSON SCHEMAStartProtectedQueryResponse
JSON SCHEMAScroll within the panel for all 17 ·
JSON Structure 17
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Clean Rooms Collaboration Structure
JSON STRUCTUREClean Rooms Configured Table Structure
JSON STRUCTUREClean Rooms Create Collaboration Request Structure
JSON STRUCTUREClean Rooms Create Collaboration Response Structure
JSON STRUCTUREClean Rooms Create Configured Table Request Structure
JSON STRUCTUREClean Rooms Create Configured Table Response Structure
JSON STRUCTUREClean Rooms Create Membership Request Structure
JSON STRUCTUREClean Rooms Create Membership Response Structure
JSON STRUCTUREClean Rooms Get Collaboration Response Structure
JSON STRUCTUREClean Rooms List Collaborations Response Structure
JSON STRUCTUREClean Rooms List Configured Tables Response Structure
JSON STRUCTUREClean Rooms List Memberships Response Structure
JSON STRUCTUREClean Rooms List Protected Queries Response Structure
JSON STRUCTUREClean Rooms Membership Structure
JSON STRUCTUREClean Rooms Protected Query Structure
JSON STRUCTUREClean Rooms Start Protected Query Request Structure
JSON STRUCTUREClean Rooms Start Protected Query Response Structure
JSON STRUCTUREScroll within the panel for all 17 ·
Examples 17
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Scroll within the panel for all 17 ·
Security Posture 4
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 5
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Marketing Measurement
Measure campaign effectiveness by combining advertiser and publisher data in a privacy-safe environment.
Customer Insights
Build comprehensive customer views by combining data from multiple channels and partners.
Collaborative Research
Enable multi-company research and product development with secure data sharing.
Risk Assessment
Analyze sensitive financial or health data across organizations for risk prediction without data exposure.
Audience Activation
Create and activate privacy-safe audience segments across advertising platforms.
Integrations 7
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
Amazon S3
Store and retrieve collaboration data and query results in S3.
Snowflake
Zero-ETL integration with Snowflake datasets for cross-platform collaboration.
AWS Glue
Configure Glue tables as the underlying data source for Clean Rooms configured tables.
Amazon Athena
Run analytics on collaboration results stored in S3 using Athena.
Amazon SageMaker
Apply ML models within protected Clean Rooms jobs.
AWS IAM
Control access to Clean Rooms resources with IAM policies.
AWS CloudTrail
Audit all Clean Rooms API calls via CloudTrail.
Scroll within the panel for all 7 ·
Resources
Every other property we hold for Amazon Clean Rooms — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 3
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 3
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 3
Pagination, idempotency, versioning, errors, and events
Build 2
SDKs, sample code, and the tooling you integrate with
Access & Security 6
Authentication, authorization, and security posture
Learn 1
Tutorials, courses, talks, and written guidance
Operate 4
Status, limits, changes, and where to get help
Commercial 2
Pricing, plans, and the legal terms of use
Company 2
The organization behind the API
← All providers · Data indexed from github.com/api-evangelist/amazon-clean-rooms · machine-readable index on apis.io