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AWS has updated Amazon Bedrock’s Data Automation feature, designed to streamline the creation of generative AI applications. The update includes improved support for extracting information from unstructured, multimodal data and integrates with Knowledge Bases for Retrieval Augmented Generation (RAG) use cases. New features include modality enablement, file-type-based routing, hyperlink extraction from PDFs, and an increased document page limit to 3,000 pages. These enhancements aim to provide developers with greater control and improve Bedrock’s document processing capabilities, simplifying workflows for enterprise users.

This article offers 14 practical tips to reduce cloud computing costs, emphasizing that even small optimizations can lead to significant savings. The suggestions include shutting down development clusters when idle, utilizing mock services, right-sizing instances, choosing cold storage or cheaper providers, leveraging spot instances and reserved instances, promoting transparency in spending, adopting serverless architecture, minimizing data storage, utilizing local storage options, and optimizing data location. The focus is on efficiency and making smart choices to control cloud expenses.

The article discusses the challenges platform engineering teams face managing costs and complexity in cloud computing, Kubernetes, and AI initiatives. Research shows organizations struggle with Kubernetes cost visibility and lack standardization. Legacy tools fall short in complex, multi-cloud environments, leading to budget overruns. The integration of AI workloads further complicates matters. The solution involves prioritizing automation, self-service capabilities, and cost-management strategies, enabling developers to move quickly while controlling resource usage. This approach supports sustainable platform engineering and allows teams to adapt to future challenges.