---
title: "AWS"
description: "205 posts about AWS, summarised, each linking to the original."
---

# AWS
> 205 posts about AWS, summarised, each linking to the original.

## Articles

### [A Tale of Two Flink Autoscalers](https://yomu.fyi/post/a-tale-of-two-flink-autoscalers.md)
- Company: [Netflix](https://yomu.fyi/company/netflix.md)
- Author: Netflix Technology Blog
- Published: Aug 21, 2026
- Type: Problem & solution

Netflix operates over 30,000 Apache Flink jobs across multiple AWS regions, ranging from managed pipelines to complex stateful DAGs. Its initial in-house autoscaler used external telemetry from Mantis and Atlas to scale TaskManagers as a single unit, cutting resource usage by 25–45% but failing on multi-operator stateful topologies. To address these limitations, Netflix adopted the Apache Flink Autoscaler library, which calculates True Processing Rate and per-vertex parallelism using internal metrics. The architecture uses Temporal workflows orchestrated within a Spring Boot application to evaluate jobs individually and prevent cross-job blast radiuses. Implementing the open-source autoscaler reduced annualized compute expenditures by 58% for client telemetry and logging, saving approximately $1.1 million.


### [How AgentFlo built AI sales agents with Amazon Bedrock AgentCore – Part 2](https://yomu.fyi/post/how-agentflo-built-ai-sales-agents-with-amazon-bedrock-agentcore-part-2.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Muhammad Musab Iqbal
- Published: Aug 21, 2026
- Type: Problem & solution

AgentFlo designed an architecture using Amazon Bedrock AgentCore and AWS serverless infrastructure to address traffic spikes and build trust in autonomous commerce agents. Security operates across three stages: AWS Fargate filters prompt injections and opt-outs, AgentCore Gateway enforces deterministic Cedar authorization policies during tool execution, and post-turn privacy filters screen outputs. Session isolation is maintained across merchants through AgentCore runtime microVMs, while AgentCore Observability and Amazon CloudWatch provide end-to-end tracing. Data reliability and long conversation journeys rely on Amazon DynamoDB context storage, Amazon Bedrock Knowledge Bases, and Amazon S3 Vector embeddings. Early deployment data showed a 12 percent net revenue uplift.


### [Inbound Private Link now supports account-level Genie One, the account console, and custom URLs](https://yomu.fyi/post/inbound-private-link-now-supports-account-level-genie-one-the-account.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Robert Zhang, Manish Bansal, Chen He, Yankai Zhang
- Published: Aug 20, 2026
- Type: Announcement

Databricks has expanded Inbound Private Link in Beta on AWS and Azure to cover account-level resources, including account-level Genie One, the account console, Governance Hub, and account-level APIs. The update also supports custom URLs and Managed Disaster Recovery stable URLs, while one shared General Access endpoint in any region can serve workspace and account-level UI and API resources, removing the need for one endpoint per region or workspace. These capabilities use context-based ingress, where account admins define allow and deny rules by caller identity, network source, and destination through account and workspace policies. Enabling private access to account-level resources requires registering and allowlisting a General Access endpoint with the account-policy and resolving the custom URL to that endpoint. Existing workspace-specific URLs, private access settings, and IP access lists continue to work in parallel, while service-direct and SCC relay endpoints retain per-region requirements.


### [POSETTE Talk Recap - Postgres Isn't Slow. Your Storage Is](https://yomu.fyi/post/posette-talk-recap-postgres-isn-t-slow-your-storage-is.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 20, 2026
- Type: Benchmark

At POSETTE 2026, Sai Srirampur’s talk examines how storage limits can produce slow ingestion, erratic P95 reads, lagging autovacuum, slower checkpoints, and logical replication lag in large PostgreSQL workloads. The benchmark ran the same 3.3-billion-row, random-UPDATE workload on four local-NVMe clusters and four baseline gp3 EBS clusters, using m6id.4xlarge instances, 64 clients, and 16 worker threads. NVMe reached median 16,030 TPS versus 1,734 TPS on EBS, reduced median latency from 36.9 ms to 4.0 ms, and completed the tested VACUUM and replication-backlog tasks faster. The analysis attributes most of the gap to storage waits rather than CPU work, while the proposed production model combines local NVMe with quorum-based replication, external base backups, and archived WAL for recovery.


### [How AgentFlo built AI sales agents with Amazon Bedrock AgentCore – Part 1](https://yomu.fyi/post/how-agentflo-built-ai-sales-agents-with-amazon-bedrock-agentcore-part.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Muhammad Musab Iqbal
- Published: Aug 20, 2026
- Type: Problem & solution

AgentFlo built autonomous AI sales agents to address cart abandonment, unassisted messaging channels, and severe traffic spikes across eCommerce platforms. The system uses the Strands Agents SDK alongside Amazon Bedrock AgentCore to manage intent, recommend products, and execute transactions. Incoming customer communications pass through AWS Fargate before entering AgentCore runtime, which executes model-driven agents inside isolated microVMs with stateful sessions lasting up to eight hours. Tool execution is brokered separately through AgentCore Gateway to ensure operations like cart modifications scale independently from model reasoning. This deployment model enables merchants to launch specialized conversational agents in minutes without pre-provisioning capacity or maintaining custom container configurations.


### [How Clario technology detects PHI/PII in DICOM images using Amazon Bedrock](https://yomu.fyi/post/how-clario-technology-detects-phi-pii-in-dicom-images-using-amazon-bed.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Alex Boudreau
- Published: Aug 19, 2026
- Type: Problem & solution

Clario developed an automated PHI and PII detection solution on AWS to process thousands of DICOM image slices and PDF files in clinical trials. Medical imaging workflows require strict privacy compliance under HIPAA, GDPR, and ICH E6 guidelines across standard tags, custom vendor metadata, and pixel-burned text. To address this, the pipeline retrieves files from Amazon S3 and runs memory-intensive detection workloads on Amazon EKS backed by Amazon RDS for PostgreSQL. Amazon Textract handles optical character recognition, while Anthropic's Claude Sonnet on Amazon Bedrock classifies sensitive data using domain-optimized prompt engineering. The system identifies precise coordinates and sensitive entity types rather than directly altering files, enabling human-in-the-loop review before downstream redaction occurs.


### [AI-powered clinical trial eligibility and safety using Amazon Bedrock AgentCore](https://yomu.fyi/post/ai-powered-clinical-trial-eligibility-and-safety-using-amazon-bedrock.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Sachin Jain
- Published: Aug 19, 2026
- Type: Problem & solution

Manual chart review across fragmented clinical sources slows clinical trial enrollment, contributing to timeline delays and high screen failure rates. To address this bottleneck, an architecture on AWS automates eligibility and safety assessments while preserving human clinician authority. AWS HealthLake ingests and normalizes records into FHIR R4 resources, while Amazon Bedrock AgentCore orchestrates pre-screening, detailed screening, and site enrollment agents. Amazon Bedrock AgentCore Evaluations scores screening determinations using an LLM-as-a-judge approach for clinical accuracy, operational effectiveness, and safety compliance. This human-in-the-loop workflow produces structured recommendations with source citations, reducing patient matching time from days to minutes while maintaining compliance audit trails.


### [How Sony LIV uses ClickHouse Cloud to deliver live streaming analytics at billion-row scale](https://yomu.fyi/post/how-sony-liv-uses-clickhouse-cloud-to-deliver-live-streaming-analytics.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 18, 2026
- Type: Problem & solution

Sony LIV’s legacy analytics environment combined batch pipelines, Elasticsearch, BigQuery, and other telemetry stores, creating slow, costly, fragmented analysis as streaming activity reached billions of events per day. During live sports, the platform needed sub-second queries, high ingestion throughput, cost-efficient long-term storage, and horizontal scaling for sudden traffic spikes. Sony LIV consolidated these workloads on ClickHouse Cloud: client, streaming, CDN, and advertising systems send events through Amazon Kinesis and ClickPipes into a ClickHouse cluster serving dashboards, product analytics, QoE, and SRE workflows. The source reports that queries previously taking tens of seconds to minutes now run in sub-second to low-single-digit seconds, improving operational responsiveness, engineering productivity, and the handling of major live-event traffic.


### [Consistency is the new latency: AI at the data layer](https://yomu.fyi/post/consistency-is-the-new-latency-ai-at-the-data-layer.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Suman Chatterjee
- Published: Aug 18, 2026
- Type: Problem & solution

Autonomous AI agents depend on the accuracy of the underlying data layer because retrieved data populates the LLM context window for multi-step reasoning. In distributed architectures, standard asynchronous replication lag causes agents to ingest stale data, leading to flawed execution and self-reinforcing hallucination debt when errors are saved back to memory. To resolve this, architectures must align replication strategies with specific task truth requirements across three primary patterns. High-stakes workloads utilize Amazon Aurora with global write forwarding or native Aurora DSQL for multi-region strong consistency, while high-scale scenarios leverage DynamoDB Global Tables with conditional writes to prevent lost updates. Finally, high-velocity ingestion pipelines enforce LOCAL\_QUORUM reads and writes in Amazon Keyspaces to capture critical data spikes reliably.


### [Serverless vehicle tracking at scale: Bosch L.OS on AWS](https://yomu.fyi/post/serverless-vehicle-tracking-at-scale-bosch-l-os-on-aws.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Yogish Kutkunje Pai
- Published: Aug 14, 2026
- Type: Problem & solution

Bosch Mobility Platform Solutions developed the Logistics Operating System on AWS to unify vehicle tracking across fragmented spot logistics markets. The system standardizes multiple telematics providers, disparate data formats, and diverse communication protocols into a centralized visibility layer. Orchestration is handled by a Tracking Connector running on Amazon ECS with AWS Fargate, while AWS Lambda functions act as provider-specific adapters for protocol translation. Event-driven message ingestion and streaming rely on Amazon MSK, complemented by Amazon ElastiCache for low-latency caching and Amazon DynamoDB for compliance policy enforcement. Currently operational in India with ten integrated ISVs, the platform reportedly reduces integration costs by 15–20% and lowers total tracking costs for small transporters by 25–30%.


### [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.


### [How iFood built its agentic security platform on ClickHouse Cloud](https://yomu.fyi/post/how-ifood-built-its-agentic-security-platform-on-clickhouse-cloud.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 12, 2026
- Type: Problem & solution

iFood’s security incident response team needed high-volume log ingestion, long-term retention, and rapid access to months of history, but its Databricks-based platform became costly to scale beyond 130 TB and imposed query timeouts. The team ran a production-data proof of concept and rebuilt its tooling around ClickHouse Cloud on AWS: logs land in S3 and flow through ClickPipes into ClickHouse, with Querybook preserving notebook-based investigations and ClickStack planned for operational metrics. With ClickHouse Cloud, iFood reports 9–16x faster queries, expected cost reductions of 40–50%, and data freshness improving from hourly batches to 2–10 minutes; alerts that previously ran hourly can now run about every 10 minutes. That foundation also supports agentic threat hunting: multiple sub-agents query more than 30 TB in parallel, reducing a hunt from about a week to roughly two hours, while Langfuse is used to inspect and tune agent analyses.


### [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.


### [A decade of mathematical certainty: Reflections on the Automated Reasoning Group](https://yomu.fyi/post/a-decade-of-mathematical-certainty-reflections-on-the-automated-reason.md)
- Company: amazon.science
- Author: Byron Cook
- Published: Aug 11, 2026
- Type: Explainer

Launched in 2016, the AWS Automated Reasoning Group applies mathematical logic, formal verification, and satisfiability modulo theories solvers to prove the correctness and security of cloud systems at scale. Early research prototypes such as Tiros and Zelkova evolved into production tools like Reachability Analyzer, Amazon Inspector, S3 Block Public Access, and IAM Access Analyzer to evaluate network reachability and access policies. Internally, the group has verified foundational infrastructure, including cryptographic libraries, boot code, the Nitro Confidentiality Engine, and an authorization engine handling one billion API calls per second. Requiring precise formal specifications frequently uncovered simpler, more maintainable system designs while establishing guarantees that traditional scenario-based testing cannot provide. Recent initiatives integrate automated reasoning into generative AI workflows, powering Amazon Bedrock Guardrails, agent compliance policies, and requirement verification in Kiro.


### [AWS Trainium Frontier competition: Co-design models and kernels on purpose-built AI chips](https://yomu.fyi/post/aws-trainium-frontier-competition-co-design-models-and-kernels-on-purp.md)
- Company: amazon.science
- Author: Louise Ping, John Gray, Emily Webber, Josh Longenecker
- Published: Aug 10, 2026
- Type: Announcement

Modern language model architectures evolved around specific hardware constraints, but AWS Trainium chips alter memory and compute dynamics through on-chip SBUF memory, systolic matrix multiplication, and explicit DMA control. The AWS Trainium Frontier competition invites teams to train language models from scratch under strict wall-clock time constraints to uncover optimal architectures and custom kernels for Trainium silicon. Participants start from a fifty-million-parameter dense baseline and can modify everything from the model architecture and optimizer to custom Neuron Kernel Interface routines. Phase 1 evaluates thirty-minute single-chip training runs by validation bits-per-byte, while Phase 2 grants four-hour multi-chip server access to evaluate both training efficiency and reasoning on the CORE benchmark. Top finalists will present their findings at NeurIPS 2026, compete for cash prizes, and co-publish research with Annapurna Labs.


### [How Musinsa scaled its audience engine with ClickHouse Cloud and reduced TCO by 71.4%](https://yomu.fyi/post/how-musinsa-scaled-its-audience-engine-with-clickhouse-cloud-and-reduc.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Aug 9, 2026
- Type: Problem & solution

Musinsa’s Audience Engine powers a customer data platform that targets audiences across 16.4 million members. It stores each user-to-audience relationship as a separate row, producing roughly 1.1 billion cohort-to-user mappings across 2,500 audiences and potentially 16 million new rows when one audience is added. Its self-hosted ClickHouse deployment coupled storage and compute, forced workloads to share a cluster, and created operational burdens as data and query demands grew. Musinsa migrated to ClickHouse Cloud on AWS, using separated S3-backed storage, workload-specific compute, automatic idling, vertical autoscaling, and ClickPipes for direct real-time ingestion. The company reports an 86.5% reduction in storage costs and a total cost of ownership reduction of up to 71.4%, while shifting engineering effort toward CDP business logic.


### [How Salesforce Eliminated Single-Region Risk and Reduced Downtime Blast Radius at 4B Metrics/Min](https://yomu.fyi/post/how-salesforce-eliminated-single-region-risk-and-reduced-downtime-blas.md)
- Company: [Salesforce](https://yomu.fyi/company/salesforce.md)
- Author: Scott Nyberg
- Published: Aug 5, 2026
- Type: Problem & solution

Salesforce redesigned its internal observability platform, Argus, to eliminate single-region failure risks while ingesting approximately 4 billion metrics per minute. Operating out of a single AWS region previously created a global blast radius and generated high cross-region data transfer costs. To mitigate these risks without the prohibitive expense of full multi-region replication, the engineering team implemented a geo-local architecture that processes and stores telemetry closer to its origin across production geographies. A newly introduced federation query layer relies on Elasticsearch mappings to route queries selectively to relevant regional clusters, avoiding broad fan-out. The platform also adopted metadata caching for wildcard queries and implemented HTTP 206 partial-response handling to maintain visibility when individual regions experience downtime.


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