Amazon Web Services

Amazon Web Services provides a wide range of infrastructure and application services that allow you to operate almost anything in the cloud. This includes everything from large enterprise applications and big data initiatives to social gaming and mobile applications. Discover how millions of users—ranging from rapidly growing startups and major corporations to top government organizations—are leveraging AWS to reduce expenses, enhance flexibility, and speed up innovation.

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Optimizing cost for using foundational models with Amazon Bedrock | Amazon Web Services

3 days ago | aws.amazon.com | Adam Richter |Ádám Richter |Bowen Wang

This post was made better through reviews from Andrew Shamiya and Zhibin Cao. As we continue our five-part series on optimizing costs for generative AI workloads on AWS, our third blog shifts focus to Amazon Bedrock. In our previous posts, we explored general Cloud Financial Management principles on generative AI adoption and strategies for custom model development using Amazon EC2 and Amazon SageMaker AI. Today, we’ll guide you through cost optimization techniques for Amazon Bedrock.
Choose the right throughput strategy for Amazon DynamoDB applications | Amazon Web Services

4 days ago | aws.amazon.com | Esteban Parra

AWS Database Blog Amazon DynamoDB is a fully managed key-value and document database designed to deliver consistent single-digit millisecond performance at any scale. When getting started with DynamoDB, one of the first decisions you will make is choosing between two throughput modes: on-demand and provisioned.
How Amazon Finance Automation built an operational data store with AWS purpose built databases to power critical finance applications | Amazon Web Services

1 week ago | aws.amazon.com | Pradeep Misra |Nitin Arora |Kumar Gaurav |Javier Vega

Amazon Finance Automation (FinAuto) is the tech organization of Amazon Finance Operations (FinOps). Its mission is to enable FinOps to support the growth and expansion of Amazon businesses. It works as a force multiplier through automation and self-service, while providing accurate and on-time payments and collections.
How Heroku migrated hundreds of thousands of self-managed PostgreSQL databases to Amazon Aurora | AWS Database Blog

2 weeks ago | aws.amazon.com

In this post, we discuss how Heroku migrated their multi-tenant PostgreSQL database fleet from self-managed PostgreSQL on Amazon Elastic Compute Cloud (Amazon EC2) to Amazon Aurora PostgreSQL-Compatible Edition. Heroku completed this migration with no customer impact, increasing platform reliability while simultaneously reducing operational burden. We dive into Heroku and their previous self-managed architecture, the new architecture, how the migration of hundreds of thousands of databases was performed, and the enhancements to the customer experience since its completion.
Enhanced Network Security Control: Flow Management with AWS Network Firewall | Amazon Web Services

2 weeks ago | aws.amazon.com | Hardik Shah |Amish Shah |Pranav Bhardwaj |Cody Williams

AWS Network Firewall is a managed, stateful network firewall and intrusion detection and prevention service. It allows you to implement security rules for fine-grained control of your VPC network traffic. In this blog post, we discuss flow capture and flow flush, new features of AWS Network Firewall that enhance network visibility and security policy enforcement.
Optimizing cost savings: The advantage of Amazon Aurora over self-managed open source databases | AWS Database Blog

2 weeks ago | aws.amazon.com

In this post, we highlight often overlooked architectural designs and the inherent features of Aurora that optimize costs when deploying an open source database. The following sections examine various use cases, contrasting typical self-managed database configurations and their associated costs with the equivalent solution on Aurora, highlighting potential cost savings and operational efficiencies.
Evaluate models or RAG systems using Amazon Bedrock Evaluations – Now generally available | Amazon Web Services

3 weeks ago | aws.amazon.com | Adewale Akinfaderin |Ishan Singh |Jesse Manders |Shreyas Vathul Subramanian

AWS Machine Learning Blog Organizations deploying generative AI applications need robust ways to evaluate their performance and reliability. When we launched LLM-as-a-judge (LLMaJ) and Retrieval Augmented Generation (RAG) evaluation capabilities in public preview at AWS re:Invent 2024, customers used them to assess their foundation models (FMs) and generative AI applications, but asked for more flexibility beyond Amazon Bedrock models and knowledge bases.
Improve PostgreSQL performance using the pgstattuple extension | Amazon Web Services

3 weeks ago | aws.amazon.com | Vivek Singh |Kiran Singh |Sagar Patel

As businesses continue to generate and store vast amounts of data, the need for efficient and reliable database management systems becomes increasingly important. PostgreSQL, an open source relational database management system (RDBMS), has established itself as a powerful solution for handling complex data requirements. One of the key strengths of PostgreSQL lies in it’s extensibility.
Say Hello to 208 New AWS Competency, Service Delivery, Service Ready, and MSP Partners Added in March | AWS Partner Network (APN) Blog

3 weeks ago | aws.amazon.com

We are excited to highlight 208 AWS Partners that received new or renewed specializations in March for our global AWS Competency, AWS Managed Service Provider (MSP), AWS Service Delivery, and AWS Service Ready programs. These designations span workload, solution, and industry, and help AWS customers identify top AWS Partners that can deliver on core business objectives. AWS Partners are focused on your success, helping customers take full advantage of the business benefits AWS has to offer.
Optimizing cost for building AI models with Amazon EC2 and SageMaker AI | Amazon Web Services

4 weeks ago | aws.amazon.com | Adam Richter |Ádám Richter |Bowen Wang

This post was made better through reviews from Shane Robbins, Shruti Koparkar, and Natalia Cummings Welcome to the second blog of the series on optimizing costs for generative AI workloads on AWS. In our first blog, we provided an overview of different implementation approaches and Cloud Financial Management principles for generative AI adoption.