Category: Generative AI

In this post, we’ll look at Amazon Bedrock pricing through the lens of a practical, real-world example: building a customer service chatbot. We’ll break down the essential cost components, walk through capacity planning for a mid-sized call center implementation, and provide detailed pricing calculations across different foundation models.

Released on August 5, 2025, OpenAI’s GPT-OSS models, gpt-oss-20b and gpt-oss-120b, are now available on AWS through Amazon SageMaker AI and Amazon Bedrock. In this post, we walk through the process of fine-tuning a GPT-OSS model in a fully managed training environment using SageMaker AI training jobs.

Amazon Q Business is a fully managed, generative AI-powered assistant that helps enterprises unlock the value of their data and knowledge. With Amazon Q Business, you can quickly find answers to questions, generate summaries and content, and complete tasks by using the information and expertise stored across your company’s various data sources and enterprise systems. […]

In this post, we explore each component in detail, along with the technical implementation of the two core modules: course outline generation and course content generation.

In this post, we present how the Arize AX service can trace and evaluate AI agent tasks initiated through Strands Agents, helping validate the correctness and trustworthiness of agentic workflows.

In this post, we discuss how to implement a low-code no-code AIOps solution that helps organizations monitor, identify, and troubleshoot operational events while maintaining their security posture. We show how these technologies work together to automate repetitive tasks, streamline incident response, and enhance operational efficiency across your organization.

In this post, you’ll learn how you can use Amazon Q Developer command line interface (CLI) with Model Context Protocol (MCP) servers integration to modernize a legacy Java Spring Boot application running on premises and then migrate it to Amazon Web Services (AWS) by deploying it on Amazon Elastic Kubernetes Service (Amazon EKS).

In this post, we first introduce the Strands Agents SDK and its core features. Then we explore how it integrates with AWS environments for secure, scalable deployments, and how it provides rich observability for production use. Finally, we discuss practical use cases, and present a step-by-step example to illustrate Strands in action.

In this post, we introduce how to combine Strands Agents with Tavily’s purpose-built web intelligence API, to create powerful research agents that excel at complex information gathering tasks while maintaining the security and compliance standards required for enterprise deployment.

This blog post explores how to create powerful agentic applications using the Amazon Bedrock FMs, LangGraph, and the Model Context Protocol (MCP), with a practical scenario of handling a GitHub workflow of issue analysis, code fixes, and pull request generation.