You are not allowed to take anything produced by Amazon Bedrock — including AI-generated text, images, or other outputs — and use it to build a competing AI service or foundation model.
This analysis describes what AWS Bedrock's agreement states, permits, or reserves. It does not constitute a legal determination about enforceability. Regulatory applicability and practical outcomes may vary by jurisdiction, enforcement context, and individual circumstances. Read our methodology
This clause establishes a non-compete restriction on the outputs and intellectual property generated through the service. It operationally constrains downstream model development activities that would create competitive alternatives to Amazon's AI offerings.
The updated terms remove the explicit reference to a Data Processing Addendum governing Anthropic data transfers, though the requirement to obtain opt-in consent for Anthropic model use remains in place. For Kiro users, the revised language now explicitly states that abuse detection includes retention and potential human review of inputs and outputs when selecting certain models. Users of Anthropic models on Bedrock should verify opt-in consent mechanisms in service documentation to understand data handling practices.
View change record →The updated terms establish a new project-based service structure for AWS (new) users, effective August 17, 2026, with new rules governing team member access and content ownership. Users who enable spend limits agree that AWS may suspend their account or project access upon reaching their limit, and may permanently close the account or project if not reactivated within an unspecified timeframe. Contributed content by team members becomes the project owner's property and is subject to a nonexclusive irrevocable license granted to all project members. You can configure AI services opt-out policies through AWS Settings; project owners can manage team member permissions and access controls.
View change record →The updated terms now explicitly state that AWS IoT SiteWise Scenario Discovery is not designed for real-time vehicle control and cannot be used as the sole basis for determining vehicle safety or regulatory compliance. Organizations deploying this service must implement independent human monitoring and safety validation before using its outputs to support vehicle system decisions. The terms make clear that AWS assumes no responsibility for uses that violate these constraints.
View change record →Businesses using Bedrock cannot feed its outputs into training pipelines for competing AI models, which constrains AI product development strategies and may require costly technical controls to ensure pipeline segregation.
How other platforms handle this
We do not train any models on User Content.
the Company reserves the right, but not the obligation, to review or monitor Inputs and Outputs using automated and manual tools.
How Adobe analyzes your content using techniques such as machine learning in order to improve our Services and Software, and how to opt out of this.
"You may not use Amazon Bedrock, including any content, models, or model outputs, to develop foundation models or other large scale models that compete with Amazon Bedrock or Amazon's other AI/ML services.Excerpt from AWS Bedrock's AWS Service Terms
REGULATORY FRAMEWORK: This provision engages intellectual property law (17 U.S.C.
Enforcement risk, jurisdiction flags, contract triggers, and due diligence action items.
How Meta, TikTok, and Supabase restructured governance language across documents, jurisdictions, and consent frameworks through incremental document updates.
How 10 AI platforms describe the use of user data for model training, improvement, and development, based on archived governance provisions.
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This clause establishes a non-compete restriction on the outputs and intellectual property generated through the service. It operationally constrains downstream model development activities that would create competitive alternatives to Amazon's AI offerings.
Businesses using Bedrock cannot feed its outputs into training pipelines for competing AI models, which constrains AI product development strategies and may require costly technical controls to ensure pipeline segregation.
ConductAtlas has identified this type of provision across 216 platforms. See the full comparison.
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