AWS Architecture Blog

Learn how parameterized query templates reduced Text2SQL latency by 80% and cut token consumption by over 50%. This post covers the architecture behind an intelligent caching layer that uses semantic similarity to match user questions to SQL templates, bypassing expensive LLM calls.

Learn how Adobe Firefly achieved 28x faster GPU metric queries by migrating from self-managed Prometheus to Amazon Managed Service for Prometheus, with improvements in query performance, infrastructure reliability, and operational efficiency.

Learn how to coordinate AI agents through shared state in Amazon S3 instead of a central orchestrator. Deploy and observe self-organizing agent clusters on Amazon EC2 with the open-source kiro-flock reference implementation.

Healthcare organizations face a critical scaling challenge – how to maintain deeply personalized patient interactions as member bases grow, without overwhelming care teams or compromising quality. At Pelago, a digital health company specializing in substance use disorder support, the engineering team found a way to build an AI-powered solution to address this challenge using AWS […]

AWS CloudFormation is the foundational tool of infrastructure-as-code for thousands of organizations running workloads on AWS. But as teams push the boundaries of what CloudFormation can do natively, custom resources have emerged as a powerful extension mechanism that unlocks a broad range of possibilities. Yet, when it comes to building resilient, multi-Region deployments with custom […]

According to Siemens’ 2024 report The True Cost of Downtime, Fortune 500 companies lose an estimated $1.4 trillion annually because of unplanned downtime. This downtime is often worsened by a lack of skills to detect and resolve issues quickly. Generative AI offers a promising path to address this, but deploying these capabilities in industrial environments […]

Organizations in regulated industries like financial services, insurance, healthcare, and government ingest large volumes of data containing personally identifiable information (PII). Your applications, claims processing systems, partner data feeds, and internal workflows produce files that may include names, addresses, Social Security numbers, and domain-specific identifiers such as policy numbers, member IDs, and medical record numbers. […]

When you deploy Eclipse Dataspace Components (EDC) connectors on AWS, one of the first challenges you face is predicting and controlling the cost of the required infrastructure. Without clear benchmarks, it is difficult to make informed decisions about workload sizing, environment configuration, and long-term investment. Part 1 of this 3-part blog series covered the fundamentals […]

Running Eclipse Dataspace Components (EDC) connectors in production on AWS requires deliberate architecture decisions around isolation, managed services, and security layering. In Part 1 of this series, we covered the fundamentals of data space architectures and EDC per the International Data Space Association’s (IDSA) standards. If you are new to EDC, we recommend starting there. […]

This three-part blog series guides you through implementing Eclipse Dataspace Components (EDC) on AWS, from foundational concept to production deployment. Part 1 establishes the theoretical foundation with IDSA standards, the Dataspace Protocol (DSP), and core EDC architecture. Part 2 provides production-ready AWS deployment patterns using services like Amazon Elastic Container Service (Amazon ECS), Amazon Aurora, […]