AI

Part 2: Amazon Bedrock cost attribution with Amazon Athena and CUDOS

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

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How OneAdvanced deployed over 50 AI agents on UK-sovereign AWS

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

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Pay with confidence: How Solv Labs built verifiable, auditable agent payments on Amazon Bedrock AgentCore payments

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.

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Tiered KV cache for large LLMs on Amazon SageMaker HyperPod with Curvine

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

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