{"resourceId":"aws-bedrock-kimi-k3-1-pro-moonshot-open-weight-20261110","versions":[{"version":"external-68342420a81c71ffacec2dc3f16dca75a5abb78c99a08b31a6ab3dc78c9986fb","resource":{"id":"aws-bedrock-kimi-k3-1-pro-moonshot-open-weight-20261110","title":"AWS details Moonshot AI's Kimi K3.1 Pro Highspeed on Amazon Bedrock, with data-boundary claims and US-only routing option","organization":"Amazon Web Services / Moonshot AI","sector":"Cross-SLED AI platforms / model procurement","geography":"Global commercial AWS Regions (US and India geographic routing options)","publishedAt":"2026-11-10","publicationDate":"2026-11-10","eventDate":"2026-11-06","sourceName":"AWS Artificial Intelligence Blog","sourceLabel":"Introducing Kimi K3.1 Pro on Amazon Bedrock","sourceUrl":"https://aws.amazon.com/blogs/machine-learning/introducing-kimi-k3-1-pro-on-amazon-bedrock/","evidenceClass":"vendor-claim","outcomeClass":"emerging","topics":["governance-procurement","data-security","developers-agents"],"finding":"AWS's November 10, 2026 launch post says Kimi K3.1 Pro Highspeed, an open-weight model from China-based Moonshot AI with a 1-million-token context window, is on Amazon Bedrock (AWS's What's New entry is dated November 6). AWS states customer data is processed within the AWS data boundary, is not shared with Moonshot AI, is not used for training, and that zero data retention is always on. Access is through cross-Region inference: a global profile (cheapest, any commercial Region), a US profile and an India profile. A Flex tier is offered at half the Standard price. Performance claims are attributed to Moonshot AI; no benchmarks or GovCloud availability are given.","sledRelevance":"Bedrock is a common route for state agencies and universities to reach many models through one contract. A Chinese-origin model now appears in that catalog, which matters because several states restrict PRC-affiliated AI vendors (see the Texas item in this edition). SLED cloud teams need to decide whether hosting by AWS changes how those rules apply.","evidence":"Vendor blog. Data-handling statements are AWS claims; capability claims are attributed to Moonshot AI. No independent evaluation, customer example or public-sector use is described.","architectureImplications":"Model is reached through bedrock-runtime with OpenAI-compatible Chat Completions/Responses and Bedrock Converse/Invoke APIs. The default recommended global profile can route to any commercial Region; the us. profile keeps processing in US Regions.","governanceImplications":"State policies that name Moonshot AI as a prohibited vendor may or may not reach a model hosted on AWS infrastructure; the source does not address this. Agencies should decide explicitly and enforce through model allow-lists.","securityPrivacyImplications":"AWS claims zero data retention and no operator access during inference. Model weights originate from a vendor on at least one state prohibited list; supply-chain and output-integrity questions are not addressed in the post.","caveats":"Vendor claim; no benchmarks. Interpretation of state vendor bans as applied to hosted models is ours, not the source's. GovCloud availability is not stated in the source.","streamIds":["aws"],"roles":{"sales":"Interpretation: treat this as a governance conversation, not a model upsell. The customer problem is that a Bedrock catalog now includes a model from a vendor some states prohibit, and users may pick it for cost. Stakeholders are the state CISO, procurement counsel and the cloud center of excellence. Discovery should ask whether state policy names Moonshot AI and whether it applies to cloud-hosted models. Do not claim AWS hosting satisfies any state ban; that is a legal determination the customer must make.","engineering":"Interpretation: engineering fit is about control, not adoption. Engineers should be able to block or allow moonshotai model IDs through IAM policies or service control policies, and to restrict inference profiles to the us. prefix where residency matters. Prerequisites are an approved model list and organization-level SCPs. Constraints: the recommended global profile routes worldwide, and the post does not describe GovCloud availability. Proof of value is a test showing blocked invocations fail and are logged in CloudTrail.","delivery":"Interpretation: delivery work is a model-intake control. Dependencies are a legal reading of state vendor restrictions and an owner for the Bedrock allow-list. Ownership belongs to the cloud governance team with the CISO approving exceptions. Governance checkpoints include reviewing every new Bedrock model at release and recording the decision. Proposed acceptance criteria: unapproved model IDs denied across all accounts, and decisions documented within a set number of days. Risks include shadow use through cheaper global profiles."},"roleDetails":{"sales":{"customerProblem":"A Bedrock catalog now includes a model from a vendor named on at least one state prohibited-technology list, and users may select it for cost.","stakeholders":["State or university CISO","Procurement and legal counsel","Cloud center of excellence lead"],"discoveryQuestions":["Does your state policy name Moonshot AI or PRC-affiliated AI vendors?","Does that policy reach models hosted by a US cloud provider?","Which Bedrock model IDs are allowed today, and how is that enforced?"],"valueHypothesis":"A documented allow-list decision reduces compliance risk from new catalog entries; it does not by itself prove compliance.","potentialEngagement":"A short model-governance review that maps state vendor rules to Bedrock model IDs and SCPs.","unsupportedClaims":["That AWS hosting satisfies state vendor bans","Moonshot's performance claims","Cost savings from Flex or global routing for any specific workload"]},"engineering":{"fit":"Relevant to any SLED Bedrock tenant in commercial Regions; GovCloud availability not stated.","architectureIntegration":"Bedrock runtime with OpenAI-compatible and Converse APIs; global, US and India cross-Region inference profiles.","prerequisites":["Organization-level SCPs","Approved model list","CloudTrail logging of Bedrock invocations"],"constraints":["Global profile routes to any commercial Region","GovCloud availability not stated","No benchmarks published by AWS"],"security":"Restrict model IDs and inference profiles by policy; AWS claims zero retention and no operator access, which should be confirmed in service terms.","proofOfValue":["Verify an SCP denies the model in a test account","Confirm us. profile keeps processing in US Regions per documentation","Check CloudTrail records denied calls"]},"delivery":{"work":"Add the model to the agency's Bedrock model-intake review and enforce the decision.","dependencies":["Legal interpretation of state vendor rules","SCP management access","Model inventory"],"ownership":"Cloud governance team; CISO approves exceptions.","skillsAdoption":"Cloud teams need IAM/SCP skills for model-level controls.","governanceCheckpoints":["Legal review of vendor restriction","Allow-list update","Exception approval record"],"acceptanceCriteria":["Proposed: unapproved model IDs denied in all accounts","Proposed: each new Bedrock model reviewed within an agreed period","Proposed: zero unapproved invocations in CloudTrail"],"risks":["Shadow use via cheaper profiles","Policy ambiguity on hosted models","Allow-list drift as catalog grows"]}},"retrievedAt":"2026-11-11T03:59:52Z","enrichedAt":"2026-11-11T04:05:52Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"No accessibility or workforce detail.","procurementImplications":"Lower per-token cost via the global profile and Flex tier may attract budget-constrained users, which raises the importance of allow-list controls. GovCloud availability and authorization status not stated.","operatingModelImplications":"Cloud governance teams need a standing process for reviewing each newly added Bedrock model against state vendor restrictions.","sourceVerification":{"openedUrl":"https://aws.amazon.com/blogs/machine-learning/introducing-kimi-k3-1-pro-on-amazon-bedrock/","referenceExcerpt":"Your data is processed within the AWS data boundary, isn’t shared with Moonshot AI, and isn’t used to train the model.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}