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Demystifying Amazon Bedrock Pricing for a Chatbot Assistant | Artificial Intelligence
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.
Fine-tune OpenAI GPT-OSS models on Amazon SageMaker AI using Hugging Face libraries | Artificial Intelligence
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.
How Wiz achieved near-zero downtime for Amazon Aurora PostgreSQL major version upgrades at scale using Aurora Blue/Green Deployments | AWS Database Blog
Wiz, a leading cloud security company, identifies and removes risks across major cloud platforms. Our agent-less scanner processes tens of billions of daily cloud resource metadata entries. This demands high-performance, low-latency processing, making our Amazon Aurora PostgreSQL-Compatible Edition database, serving hundreds of microservices at scale, a critical component of our architecture. In this post, we share how we upgraded our Aurora PostgreSQL database from version 14 to 16 with near-zero downtime using Amazon Aurora Blue/Green Deployments.
Scaling transaction peaks: Juspay’s approach using Amazon ElastiCache | AWS Database Blog
Juspay powers global enterprises by streamlining payment process orchestration, enhancing security, reducing fraud, and providing seamless customer experiences. In this post, we walk you through how Juspay transformed their payment processing architecture to handle transaction peaks. Using Amazon ElastiCache and Amazon RDS for MySQL, Juspay built a system that processes 7.6 million transactions per hour during peak events, achieves sub-millisecond latency, and reduces infrastructure costs by 80% compared to their previous solution.
Simplify data integration using zero-ETL from Amazon RDS to Amazon Redshift | AWS Database Blog
Organizations rely on real-time analytics to gain insights into their core business drivers, enhance operational efficiency, and maintain a competitive edge. Traditionally, this has involved the use of complex extract, transform, and load (ETL) pipelines. ETL is the process of combining, cleaning, and normalizing data from different sources to prepare it for analytics, AI, and […]
The Amazon SageMaker lakehouse architecture now automates optimization configuration of Apache Iceberg tables on Amazon S3 | AWS Big Data Blog
The Amazon SageMaker lakehouse architecture now automates optimization of Iceberg tables stored in Amazon S3 with catalog-level configuration, optimizing storage in your Iceberg tables and improving query performance. This post demonstrates an end-to-end flow to enable catalog level table optimization setting.
Create an OpenSearch dashboard with Amazon OpenSearch Service | AWS Big Data Blog
This post demonstrates how to harness OpenSearch Dashboards to analyze logs visually and interactively. With this solution, IT administrators, developers, and DevOps engineers can create custom dashboards to monitor system behavior, detect anomalies early, and troubleshoot issues faster through interactive charts and graphs.
Responsible AI for the payments industry – Part 1 | Artificial Intelligence
This post explores the unique challenges facing the payments industry in scaling AI adoption, the regulatory considerations that shape implementation decisions, and practical approaches to applying responsible AI principles. In Part 2, we provide practical implementation strategies to operationalize responsible AI within your payment systems.
Boosting search relevance: Automatic semantic enrichment in Amazon OpenSearch Serverless | AWS Big Data Blog
In this post, we show how automatic semantic enrichment removes friction and makes the implementation of semantic search for text data seamless, with step-by-step instructions to enhance your search functionality.
How Amazon Bedrock powers next-generation account planning at AWS | Artificial Intelligence
In this post, we share how we built Account Plan Pulse, a generative AI tool designed to streamline and enhance the account planning process, using Amazon Bedrock. Pulse reduces review time and provides actionable account plan summaries for ease of collaboration and consumption, helping AWS sales teams better serve our customers.
Automate conversion of Oracle SQL to PostgreSQL inside Java applications with AWS SCT | AWS Database Blog
This post demonstrates how to use AWS SCT to simplify and accelerate the migration of embedded Oracle SQL code within Java applications to PostgreSQL-compatible syntax. The solution focuses on a practical use case involving a source Oracle database coupled with a sample Java application containing numerous Oracle-specific SQL statements. By using AWS SCT, developers can automate much of the schema and SQL conversion process, reducing manual effort and minimizing errors during migration.
Integrate scientific data management and analytics with the next generation of Amazon SageMaker, Part 1 | AWS Big Data Blog
In this blog post, AWS introduces a solution to a common challenge in scientific research - the inefficient management of fragmented scientific data. The post demonstrates how the next generation of Amazon SageMaker, through its Unified Studio and Catalog features, helps scientists streamline their workflow by integrating data management and analytics capabilities.
Near real-time streaming analytics on protobuf with Amazon Redshift | AWS Big Data Blog
In this post, we explore an end-to-end analytics workload for streaming protobuf data, by showcasing how to handle these data streams with Amazon Redshift Streaming Ingestion, deserializing and processing them using AWS Lambda functions, so that the incoming streams are immediately available for querying and analytical processing on Amazon Redshift.
Cost tracking multi-tenant model inference on Amazon Bedrock | Artificial Intelligence
In this post, we demonstrate how to track and analyze multi-tenant model inference costs on Amazon Bedrock using the Converse API's requestMetadata parameter. The solution includes an ETL pipeline using AWS Glue and Amazon QuickSight dashboards to visualize usage patterns, token consumption, and cost allocation across different tenants and departments.
How Handmade.com modernizes product image and description handling with Amazon Bedrock and Amazon OpenSearch Service | Artificial Intelligence
In this post, we explore how Handmade.com, a leading hand-crafts marketplace, modernized their product description handling by implementing an AI-driven pipeline using Amazon Bedrock and Amazon OpenSearch Service. The solution combines Anthropic's Claude 3.7 Sonnet LLM for generating descriptions, Amazon Titan Text Embeddings V2 for vector embedding, and semantic search capabilities to automate and enhance product descriptions across their catalog of over 60,000 items.
Pioneering AI workflows at scale: A deep dive into Asana AI Studio and Amazon Q index collaboration | Artificial Intelligence
Today, we’re excited to announce the integration of Asana AI Studio with Amazon Q index, bringing generative AI directly into your daily workflows. In this post, we explore how Asana AI Studio and Amazon Q index transform enterprise efficiency through intelligent workflow automation and enhanced data accessibility.
AI judging AI: Scaling unstructured text analysis with Amazon Nova | Artificial Intelligence
In this post, we highlight how you can deploy multiple generative AI models in Amazon Bedrock to instruct an LLM model to create thematic summaries of text responses. We then show how to use multiple LLM models as a jury to review these LLM-generated summaries and assign a rating to judge the content alignment between the summary title and summary description.
Build an AI assistant using Amazon Q Business with Amazon S3 clickable URLs | Artificial Intelligence
In this post, we demonstrate how to build an AI assistant using Amazon Q Business that responds to user requests based on your enterprise documents stored in an S3 bucket, and how the users can use the reference URLs in the AI assistant responses to view or download the referred documents, and verify the AI responses to practice responsible AI.
Discover insights from Microsoft Exchange with the Microsoft Exchange connector for Amazon Q Business | Artificial Intelligence
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. […]
GPT OSS models from OpenAI are now available on SageMaker JumpStart | Artificial Intelligence
Today, we are excited to announce the availability of Open AI’s new open weight GPT OSS models, gpt-oss-120b and gpt-oss-20b, from OpenAI in Amazon SageMaker JumpStart. With this launch, you can now deploy OpenAI’s newest reasoning models to build, experiment, and responsibly scale your generative AI ideas on AWS. In this post, we demonstrate how to get started with these models on SageMaker JumpStart.
Introducing the Amazon Bedrock AgentCore Code Interpreter | Artificial Intelligence
In this post, we introduce the Amazon Bedrock AgentCore Code Interpreter, a fully managed service that enables AI agents to securely execute code in isolated sandbox environments. We discuss how the AgentCore Code Interpreter helps solve challenges around security, scalability, and infrastructure management when deploying AI agents that need computational capabilities.
Observing and evaluating AI agentic workflows with Strands Agents SDK and Arize AX | Artificial Intelligence
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.
Introducing Amazon Bedrock AgentCore Browser Tool | Artificial Intelligence
In this post, we introduce the newly announced Amazon Bedrock AgentCore Browser Tool. We explore why organizations need cloud-based browser automation and the limitations it addresses for FMs that require real-time data access. We talk about key use cases and the core capabilities of the AgentCore Browser Tool. We walk through how to get started with the tool.
Containerize legacy Spring Boot application using Amazon Q Developer CLI and MCP server | Artificial Intelligence
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).
Building AIOps with Amazon Q Developer CLI and MCP Server | Artificial Intelligence
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.
Introducing the Amazon DynamoDB data modeling MCP tool | AWS Database Blog
To help you move faster with greater confidence, we’re introducing a new DynamoDB data modeling tool, available as part of our DynamoDB Model Context Protocol (MCP) server. The DynamoDB MCP data modeling tool integrates with AI assistants that support MCP, providing a structured, natural-language-driven workflow to translate application requirements into DynamoDB data models. In this post, we show you how to generate a data model in minutes using this new data modeling tool.
Introducing Extended Support for Amazon ElastiCache version 4 and version 5 for Redis OSS | AWS Database Blog
Amazon ElastiCache now offers Extended Support so that you can upgrade to a new major version at a pace that meets your business requirements. Extended Support is a paid offering that provides critical security updates, bug fixes, and continued support for ElastiCache versions 4 and 5 for Redis OSS through January 31, 2029. Starting on February 1, 2026, ElastiCache Redis OSS v4 and v5 clusters that haven’t been upgraded will be automatically enrolled in Extended Support to provide continuous availability and security. In this post, we discuss what ElastiCache Extended Support entails, its key benefits, and the upgrade options available.
Improve PostgreSQL performance: Diagnose and mitigate lock manager contention | AWS Database Blog
Are your database read operations unexpectedly slowing down as your workload scales? Many organizations running PostgreSQL-based systems encounter performance bottlenecks that aren’t immediately obvious. When many concurrent read operations access tables with numerous partitions or indexes, they can even exhaust PostgreSQL’s fast path locking mechanism, forcing the system to use shared memory locks. The switch […]
Amazon Redshift out-of-the-box performance innovations for data lake queries | AWS Big Data Blog
In this post, we first briefly review how planner statistics are collected and what impact they have on queries. Then, we discuss Amazon Redshift features that deliver optimal plans on Iceberg tables and Parquet data even with the lack of statistics. Finally, we review some example queries that now execute faster because of these latest Amazon Redshift innovations.
Automate data lineage in Amazon SageMaker using AWS Glue Crawlers supported data sources | AWS Big Data Blog
In this post, we explore its real-world impact through the lens of an ecommerce company striving to boost their bottom line. To illustrate this practical application, we walk you through how you can use the prebuilt integration between SageMaker Catalog and AWS Glue crawlers to automatically capture lineage for data assets stored in Amazon Simple Storage Service (Amazon S3) and Amazon DynamoDB.
Introducing AWS Batch Support for Amazon SageMaker Training jobs | Artificial Intelligence
AWS Batch now seamlessly integrates with Amazon SageMaker Training jobs. In this post, we discuss the benefits of managing and prioritizing ML training jobs to use hardware efficiently for your business. We also walk you through how to get started using this new capability and share suggested best practices, including the use of SageMaker training plans.