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Part 1 introduced granular cost attribution for Amazon Bedrock. This feature automatically traces every inference request back to the IAM principal that made the call. It showed how the new line_item_iam_principal column can give you per-user and per-application visibility. With optional cost allocation tags, you can also aggregate spend by team, project, or tenant using […]

This post is co-authored with OneAdvanced team Deploying AI agents on a United Kingdom (UK)-sovereign AWS architecture requires careful decisions about model hosting, data residency, and agent orchestration. OneAdvanced, a UK-based enterprise software provider serving over 10,000 customers, needed to deliver AI capabilities while making sure that no data would leave the UK. At the

This post is co-written with Patrick Duffy from Solv Labs and Houman Shadab from ICME Labs Solv Labs built an AI agent-payments workflow using Amazon Bedrock AgentCore payments, a capability of Amazon Bedrock AgentCore, governed by two layers: ORACLE (Solv’s policy engine) and ICME PreFlight for compliance verification. AgentCore payments provides the payment processing infrastructure.

Running large language model (LLM) inference at scale typically forces a KV cache trade-off: you either pay for oversized GPU instances to accommodate a growing KV cache, or you accept slow time-to-first-token (TTFT) as identical prompts get recomputed on every request. For teams deploying a broad catalog of publicly available foundation models (FMs), such as

Cyber defenders have never had more capability at their fingertips, and they have never needed it more. Frontier models can now reason across an entire code base, trace a vulnerability to its root cause, and propose a fix in minutes. Those same capabilities are available to adversaries. This is why the window between a vulnerability

Google introduces AMIE for real-time clinical video consultations in simulated settings. ​ 

This post was co-written by ONESTRUCTION, Inc. and Amazon Web Services Japan G.K. as part of GENIAC (Generative AI Accelerator Challenge) Phase 3, with technical advisory from the AWS Generative AI Innovation Center (GenAIIC). Building domain-specialized foundation models in data-scarce fields is hard. You need enough training data, specialized knowledge, and ways to verify your

This post is co-written with Ry Rainey and Graham Gibson from Pixieset. Photographers and artists are among the most skeptical audiences for generative AI. They have watched it threaten their craft and flood their industry with synthetic work. A 2025 MIT study found 95% of enterprise Generative AI pilots deliver zero measurable returns. Pixieset is

This post is co-written with Mark Himelfarb and Garrett Wilkerson from First Orion. First Orion’s engineering teams were shipping faster than quality assurance (QA) could test, until Amazon Nova Act transformed QA automation. As a branded communications company whose solutions reach hundreds of millions of phone calls across carriers in the US, Canada, UK, and

AI administrators deploying Claude Code and Claude Desktop across their workforce need centralized controls over authentication, model access, cost attribution, and spend enforcement. These controls reduce operational overhead and apply governance consistently at scale. Claude apps gateway provides a self-hosted governance layer between these applications and Amazon Bedrock or Claude Platform on AWS. Building on