---
title: "AWS"
description: "Cloud computing platform providing infrastructure, storage, databases, networking, AI, analytics, security, and application services."
---

# AWS
> Cloud computing platform providing infrastructure, storage, databases, networking, AI, analytics, security, and application services.

## Articles

### [Track generative AI costs with Amazon Bedrock inference profiles](https://yomu.fyi/post/track-generative-ai-costs-with-amazon-bedrock-inference-profiles.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Erik Mack
- Published: Aug 13, 2026
- Type: Tutorial

When multiple departments share a single foundation model in Amazon Bedrock under one IAM role, usage aggregates into one billing line item, complicating department-level chargebacks and budget tracking. This issue can be resolved by deploying Amazon Bedrock application inference profiles wrapped around the shared foundation model and tagging each profile for cost allocation. The application layer determines the user department during authentication and passes the corresponding inference profile ARN as the model identifier. Once the designated cost allocation tag is activated in the AWS Billing and Cost Management console, usage separates into per-department line items in AWS Cost Explorer. This method incurs standard per-token model rates without extra charges for cost attribution.


### [Recovery strategies to meet data residency requirements](https://yomu.fyi/post/recovery-strategies-to-meet-data-residency-requirements.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Jamie Pasterick
- Published: Aug 13, 2026
- Type: Explainer

Organizations operating under data residency requirements must balance jurisdictional compliance with disaster recovery objectives for critical workloads on AWS. In locations served by a single Region or facing multi-Region constraints, three architectural patterns offer distinct trade-offs between regulatory adherence and operational complexity. The cryptographic boundary approach replicates encrypted data to another AWS Region while restricting decryption access using AWS Key Management Service policies. The data boundary pattern deploys AWS Outposts on-premises within the source country while relying on an external parent Region control plane. Finally, strict local autonomy boundaries retain data and control planes entirely in-country through on-premises systems or multi-cloud lifeboats, requiring rigorous end-to-end continuous testing.


### [Reducing Text2SQL latency with parameterized query templates](https://yomu.fyi/post/reducing-text2sql-latency-with-parameterized-query-templates.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Yury Brukau
- Published: Aug 13, 2026
- Type: Problem & solution

In Text2SQL systems, generating SQL on the fly with frontier foundation models can cause response times of 25 to 30 seconds due to 60,000-token prompts and model latency. To solve this without degrading accuracy, an intelligent caching layer was built using parameterized SQL query templates paired with question embeddings. When an incoming question matches a cached template via semantic search above a confidence threshold, named entity recognition extracts values to populate template placeholders, bypassing the initial SQL generation step. In production, this approach reached a 60% cache hit rate after two weeks, cutting per-hit token consumption by roughly 90% and reducing overall latency by 80% to under 5 seconds.


### [Adobe Firefly: Simplified observability with Amazon Managed Prometheus](https://yomu.fyi/post/adobe-firefly-simplified-observability-with-amazon-managed-prometheus.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Dev Arora
- Published: Aug 13, 2026
- Type: Problem & solution

Adobe Firefly powers generative creative features using a large-scale GPU training infrastructure hosted on Amazon Elastic Kubernetes Service. As workloads scaled to thousands of nodes and GPUs, Adobe's self-managed Prometheus setup struggled with query latency across high-cardinality telemetry data. To resolve these performance bottlenecks, the team moved two million critical time series metrics to Amazon Managed Service for Prometheus while using managed scrapers alongside their existing infrastructure. This setup accelerated query response times by more than 28x over 24-hour windows, reducing query execution times to approximately ten seconds. Additionally, the migration expanded practical monitoring windows from six hours to 24 hours, giving teams reliable visibility across long-running distributed training jobs.


### [Scaling patterns for self-organizing multi-agent clusters with Kiro](https://yomu.fyi/post/scaling-patterns-for-self-organizing-multi-agent-clusters-with-kiro.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Ivo Kammerath
- Published: Aug 11, 2026
- Type: Explainer

Traditional multi-agent architectures rely on a central supervisor agent to decompose tasks and aggregate results, creating single points of failure and context-window throughput bottlenecks. To address these scaling limits on decomposable workloads, kiro-flock implements self-organizing clusters of headless Kiro CLI agents on Amazon EC2 coordinated entirely through shared Amazon S3 state. Agents operate in bounded peer topologies such as logical rings, reading neighbor append-only logs and generating artifacts without direct inter-agent messaging or central brokers. Each agent starts fresh per iteration without conversational memory, reading current direction and peer logs to prevent drift and preserve steering control. This shared-state pattern enables resilient parallelism for tasks requiring diverse independent contributions, such as code reviews or codebase migrations.


### [Building a serverless AI assistant at Pelago: concept to care in two weeks](https://yomu.fyi/post/building-a-serverless-ai-assistant-at-pelago-concept-to-care-in-two-we.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Anton Aleksandrov
- Published: Jul 22, 2026
- Type: Problem & solution

Pelago engineered a serverless, event-driven AI assistant on AWS within two weeks to provide substance use recovery coaches with contextual response suggestions. Operating under strict Protected Health Information constraints, the system maintains clinical safety through human-in-the-loop review and keeps data confined to an Amazon VPC. Incoming messages flow via AWS AppSync and Amazon DynamoDB to an Amazon SNS topic, which fans out to independent AWS Lambda functions. The asynchronous Chat Assistant Lambda queries history, invokes Amazon Bedrock, and persists suggestions into Amazon RDS MySQL in under 10 seconds. When coaches access a conversation via Amazon API Gateway, pre-generated suggestions load in under 100 milliseconds.


### [Building multi-Region resiliency for AWS CloudFormation custom resource deployment](https://yomu.fyi/post/building-multi-region-resiliency-for-aws-cloudformation-custom-resourc.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Raman Pujani
- Published: Jul 22, 2026
- Type: Problem & solution

AWS CloudFormation custom resources extend template provisioning capabilities but lack built-in multi-Region coordination, risking duplicate executions and unhandled failures during regional outages. To solve this limitation, an active-active architecture leverages Amazon Simple Notification Service topics to fan out lifecycle events across primary and secondary infrastructure Regions. The primary Region processes events immediately, whereas the secondary Region processes incoming messages after a deliberate delay implemented via Amazon Simple Queue Service. Coordination relies on Amazon DynamoDB Global Tables with conditional writes to manage distributed locking and enforce idempotency. When failures occur in the primary Region, Amazon CloudWatch alarms signal Amazon Application Recovery Controller to automate failover without manual intervention.


### [Architecting offline-first generative AI applications for edge deployments using AWS services](https://yomu.fyi/post/architecting-offline-first-generative-ai-applications-for-edge-deploym.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Ahmed Ewis
- Published: Jul 22, 2026
- Type: Problem & solution

Industrial edge environments often face significant unplanned downtime yet lack reliable cloud connectivity to run large-scale artificial intelligence models. To address this limitation, an offline-first reference architecture shifts generative inference to edge devices while leveraging cloud services for preparation and lifecycle management. The workflow uses Amazon Bedrock with Amazon Nova Pro to create labeled dataset pairs, Amazon SageMaker AI Pipelines to fine-tune a compact model, and AWS IoT Greengrass to orchestrate edge deployments. Local execution relies on Ollama, Strands Agents, and a CPU-based ChromaDB retrieval pipeline that consumes zero GPU VRAM. In evaluations across three foundation model judges, the fine-tuned gpt-oss-20b model combined with retrieval-augmented generation consistently outperformed the base model configuration across accuracy, completeness, and relevance metrics.


### [Automate custom PII detection at scale with Amazon Macie and Step Functions](https://yomu.fyi/post/automate-custom-pii-detection-at-scale-with-amazon-macie-and-step-func.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Aishwariya Khiani
- Published: Jul 22, 2026
- Type: Tutorial

Regulated organizations ingesting large data volumes must detect and classify standard and domain-specific personally identifiable information to satisfy compliance mandates like GDPR, HIPAA, CCPA, and PCI DSS. To eliminate manual inspection, an event-driven architecture orchestrates Amazon Macie scans for files landing in Amazon S3. Amazon EventBridge captures upload events and invokes AWS Step Functions, which manages Lambda functions to stage objects, trigger Macie classification jobs with custom regex identifiers, and poll for results. The pipeline separates data across a three-bucket pattern representing raw, staged, and scanned states to prevent mixing unprocessed data with validated assets. Finally, the workflow exports timestamped compliance reports in CSV and JSON formats while alerting security teams via Amazon SNS on high-severity findings.


### [Eclipse Dataspace Components on AWS: Cost optimization strategies](https://yomu.fyi/post/eclipse-dataspace-components-on-aws-cost-optimization-strategies.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Jorge Hernández Suárez
- Published: Jul 17, 2026
- Type: Explainer

Deploying Eclipse Dataspace Components (EDC) connectors on AWS presents challenges in forecasting infrastructure expenses without clear benchmarks. Estimated costs differ substantially between business-critical setups and non-critical environments based on compute and database sizing. A business-critical configuration relying on Amazon Aurora PostgreSQL db.r6g.large and standard Amazon ECS with AWS Fargate totals an estimated 387.00 USD monthly in the eu-south-2 region. Sizing down to db.t4g.medium and switching to AWS Fargate Spot lowers the non-critical monthly expense to 164.00 USD, representing a 58% reduction. Core cost drivers remain baseline infrastructure components, while storage, API calls, and data transfers contribute marginally under baseline assumptions.


[Newer posts](https://yomu.fyi/company/aws/page/2.md)