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Ten years ago, we announced the general availability of Amazon Aurora, a database that combined the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases.

As Jeff described it in its launch blog post: “With storage replicated both within and across three Availability Zones, along with an update model driven by quorum writes, Amazon Aurora is designed to deliver high performance and 99.99% availability while easily and efficiently scaling to up to 64 TiB of storage.”

When we started developing Aurora over a decade ago, we made a fundamental architectural decision that would change the database landscape forever: we decoupled storage from compute. This novel approach enabled Aurora to deliver the performance and availability of commercial databases at one-tenth the cost.

This is one of the reasons why hundreds of thousands of AWS customers choose Aurora as their relational database.

Today, I’m excited to invite you to join us for a livestream event on August 21, 2025, to celebrate a decade of Aurora database innovation.

A brief look back at the past
Throughout the evolution of Aurora, we’ve focused on four core innovation themes: security as our top priority, scalability to meet growing workloads, predictable pricing for better cost management, and multi-Region capabilities for global applications. Let me walk you through some key milestones in the Aurora journey.

We previewed Aurora at re:Invent 2014, and made it generally available in July 2015. At launch, we presented Aurora as “a new cost-effective MySQL-compatible database engine.”

In June 2016, we introduced reader endpoints and cross-Region read replicas, followed by AWS Lambda integration and the ability to load tables directly from Amazon S3 in October. We added database cloning and export to Amazon S3 capabilities in June 2017 and full compatibility with PostgreSQL in October that year.

The journey continued with the serverless preview in November 2017, which became generally available in August 2018. Global Database launched in November 2018 for cross-Region disaster recovery. We introduced blue/green deployments to simplify database updates, and optimized read instances to improve query performance.

In 2023, we added vector capabilities with pgvector for similarity search for Aurora PostgreSQL, and Aurora I/O-Optimized to provide predictable pricing with up to 40 percent cost savings for I/O-intensive applications. We launched Aurora zero-ETL integration with Amazon Redshift which enables near real-time analytics and ML using Amazon Redshift on petabytes of transactional data from Aurora by removing the need for you to build and maintain complex data pipelines that perform extract, transform, and load (ETL) operations. This year we added Aurora MySQL zero-ETL integration with Amazon Sagemaker, enabling near real-time access of your data in the lakehouse architecture of SageMaker to run a broad range of analytics.

In 2024, we made it as effortless as just one click to select Aurora PostgreSQL as a vector store for Amazon Bedrock Knowledge Bases and launched Aurora PostgreSQL Limitless Database, a serverless horizontal scaling (sharding) capability.

To simplify scaling for customers, we also increased the maximum storage to 128 TiB in September 2020, allowing many applications to operate within a single instance. Last month, we’ve further simplified scaling by doubling the maximum storage to 256 TiB, with no upfront provisioning required and pay-as-you-go pricing based on actual storage used. This enables even more customers to run their growing workloads without the complexity of managing multiple instances while maintaining cost efficiency.

Most recently, at re:Invent 2024, we announced Amazon Aurora DSQL, which became generally available in May 2025. Aurora DSQL represents our latest innovation in distributed SQL databases, offering active-active high availability and multi-Region strong consistency. It’s the fastest serverless distributed SQL database for always available applications, effortlessly scaling to meet any workload demand with zero infrastructure management.

Aurora DSQL builds on our original architectural principles of separation of storage and compute, taking them further with independent scaling of reads, writes, compute, and storage. It provides 99.99% single-Region and 99.999% multi-Region availability, with strong consistency across all Regional endpoints.

And in June, we launched Model Context Protocol (MCP) servers for Aurora, so you can integrate your AI agents with your data sources and services.

Let’s celebrate 10 years of innovation
By attending the August 21 livestream event, you’ll hear from Aurora technical leaders and founders, including Swami Sivasubramanian, Ganapathy (G2) Krishnamoorthy, Yan Leshinsky, Grant McAlister, and Raman Mittal. You’ll learn directly from the architects who pioneered the separation of compute and storage in cloud databases, with technical insights into Aurora architecture and scaling capabilities. You’ll also get a glimpse into the future of database technology as Aurora engineers share their vision and discuss the complex challenges they’re working to solve on behalf of customers.

The event also offers practical demonstrations that show you how to implement key features. You’ll see how to build AI-powered applications using pgvector, understand cost optimization with the new Aurora DSQL pricing model, and learn how to achieve multi-Region strong consistency for global applications.

The interactive format includes Q&A opportunities with Aurora experts, so you’ll be able to get your specific technical questions answered. You can also receive AWS credits to test new Aurora capabilities.

If you’re interested in agentic AI, you’ll particularly benefit from the sessions on MCP servers, Strands Agents, and how to integrate Strands Agents with Aurora DSQL, which demonstrate how to safely integrate AI capabilities with your Aurora databases while maintaining control over database access.

Whether you’re running mission-critical workloads or building new applications, these sessions will help you understand how to use the latest Aurora features.

Register today to secure your spot and be part of this celebration of database innovation.

To the next decade of Aurora innovation!

— seb

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We are excited to announce the latest cohort of AWS Heroes, recognized for their exceptional contributions and technical leadership. These passionate individuals represent diverse regions and technical specialties, demonstrating notable expertise and dedication to knowledge sharing within the AWS community. From AI and machine learning to serverless architectures and security, our new Heroes showcase the breadth of cloud innovation while fostering inclusive and engaging technical communities. Join us in welcoming these community leaders who are helping to shape the future of cloud computing and inspiring the next generation of AWS builders.

Kristine Armiyants – Masis, Armenia

Community Hero Kristine Armiyants is a software engineer and cloud support engineer who transitioned into technology from a background in finance, having earned an MBA before becoming self-taught in software development. As the founder and leader of AWS User Group Armenia for over 2.5 years, she has transformed the local tech landscape by organizing Armenia’s first AWS Community Day, scaling it from 320 to 440+ attendees, and leading a team that brings international-scale events to her country. Through her technical articles in Armenian, hands-on workshops, and “no-filter” blog series, she makes cloud knowledge more accessible while mentoring new user group organizers and early-career engineers. Her dedication to community building has resulted in five new AWS Community Builders from Armenia, demonstrating her commitment to creating inclusive spaces for learning and growth in the AWS community.

Nadia Reyhani – Perth, Australia

Machine Learning Hero Nadia Reyhani is an AI Product Engineer who integrates DevOps best practices with machine learning systems. She is a former AWS Community Builder and regularly presents at AWS events on building scalable AI solutions using Amazon SageMaker and Bedrock. As a Women in Digital Ambassador, she combines technical expertise with advocacy, creating inclusive spaces for underrepresented groups in cloud and AI technologies.

Raphael Manke – Karlsruhe, Germany

DevTools Hero Raphael Manke is a Senior Product Engineer at Dash0 and the creator of the unofficial AWS re:Invent planner, which is used to help build a schedule for the event. With a decade of AWS experience, he specializes in serverless technologies and DevTools that streamline cloud development. As the organizer of the AWS User Group in Karlsruhe and a former AWS Community Builder, he actively contributes to product enhancement through public speaking and direct collaboration with AWS service teams. His commitment to the AWS community spans from local user group leadership to providing valuable feedback to service teams.

Rowan Udell – Brisbane, Australia

Security Hero Rowan Udell is an independent AWS security consultant specializing in AWS Identity and Access Management (IAM). He has been sharing AWS security expertise for over a decade through books, blog posts, meet-ups, workshops, and conference presentations. Rowan has taken part in many AWS community programs, was an AWS Community Builder for four years, and is part of the AWS Community Day Australia Organizing Committee. A frequent speaker at AWS events including Sydney Summit and other community meetups, Rowan is known for transforming complex security concepts into simple, practical, and workable solutions for businesses securing their AWS environments.

Sangwoon (Chris) Park – Seoul, Korea

Serverless Hero Sangwoon (Chris) Park leads development at RECON Labs, an AI startup specializing in AI-driven 3D content generation. He is a former AWS Community Builder and the creator of “AWS Classroom” YouTube channel, and he shares practical serverless architecture knowledge with the AWS community. Chris hosts monthly AWS Classroom Meetups and the AWS KRUG Serverless Small Group, actively promoting serverless technologies through community events and educational content.

Toshal Khawale – Pune, India

Community Hero Toshal Khawale is an experienced technology leader with over 22 years of expertise in engineering and AWS cloud technology, holding 12 AWS certifications that demonstrate his cloud knowledge. As a Managing Director at PwC, Toshal guides organizations through cloud transformation, digital innovation, and application modernization initiatives, having led numerous large-scale AWS migrations and generative AI implementations. He was an AWS Community Builder for six years and continues to serve as the AWS User Group Pune Leader, actively fostering community engagement and knowledge sharing. Through his roles as a mentor, frequent speaker, and advocate, Toshal helps organizations maximize their AWS investments while staying at the forefront of cloud technology trends.

Learn More

Visit the AWS Heroes webpage if you’d like to learn more about the AWS Heroes program, or to connect with a Hero near you.

— Taylor

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Today, we’re announcing the general availability of Amazon Elastic VMware Service (Amazon EVS), a new AWS service that lets you run VMware Cloud Foundation (VCF) environments directly within your Amazon Virtual Private Cloud (Amazon VPC). With Amazon EVS, you can deploy fully functional VCF environments in just hours using a guided workflow, while running your VMware workloads on qualified Amazon Elastic Compute Cloud (Amazon EC2) bare metal instances and seamlessly integrating with AWS services such as Amazon FSx for NetApp ONTAP.

Many organizations running VMware workloads on premises want to move to the cloud to benefit from improved scalability, reliability, and access to cloud services, but migrating these workloads often requires substantial changes to applications and infrastructure configurations. Amazon EVS lets customers continue using their existing VMware expertise and tools without having to re-architect applications or change established practices, thereby simplifying the migration process while providing access to AWS’s scale, reliability, and broad set of services.

With Amazon EVS, you can run VMware workloads directly in your Amazon VPC. This gives you full control over your environments while being on AWS infrastructure. You can extend your on-premises networks and migrate workloads without changing IP addresses or operational runbooks, reducing complexity and risk.

Key capabilities and features

Amazon EVS delivers a comprehensive set of capabilities designed to streamline your VMware workload migration and management experience. The service enables seamless workload migration without the need for replatforming or changing hypervisors, which means you can maintain your existing infrastructure investments while moving to AWS. Through an intuitive, guided workflow on the AWS Management Console, you can efficiently provision and configure your EVS environments, significantly reducing the complexity to migrate your workloads to AWS.

With Amazon EVS, you can deploy a fully functional VCF environment running on AWS in a few hours. This process eliminates many of the manual steps and potential configuration errors that often occur during traditional deployments. Furthermore, with Amazon EVS you can optimize your virtualization stack on AWS. Given the VCF environment runs inside your VPC, you have full full administrative access to the environment and the associated management appliances. You also have the ability to integrate third-party solutions, from external storage such as Amazon FSx for NetApp ONTAP or Pure Cloud Block Store or backup solutions such as Veeam Backup and Replication.

The service also gives you the ability to self-manage or work with AWS Partners to build, manage, and operate your environments. This provides you with flexibility to match your approach with your overall goals.

Setting up a new VCF environment

Organizations can streamline their setup process by ensuring they have all the necessary pre-requisites in place ahead of creating a new VCF environment. These prerequisites include having an active AWS account, configuring the appropriate AWS Identity and Access Management (IAM) permissions, and setting up a Amazon VPC with sufficient CIDR space and two Route Server endpoints, with each endpoint having its own peer. Additionally, customers will need to have their VMware Cloud Foundation license keys ready, secure Amazon EC2 capacity reservations specifically for i4i.metal instances, and prepare their VLAN subnet information planning.

To help ensure a smooth deployment process, we’ve provided a Getting started hub, which you can access from the EVS homepage as well as a comprehensive guide in our documentation. By following these preparation steps, you can avoid potential setup delays and ensure a successful environment creation.

Let’s walk through the process of setting up a new VCF environment using Amazon EVS.

You will need to provide your Site ID, which is allocated by Broadcom when purchasing VCF licenses, along with your license keys. To ensure a successful initial deployment, you should verify you have sufficient licensing coverage for a minimum of 256 cores. This translates to at least four i4i.metal instances, with each instance providing 64 physical cores.

This licensing requirement helps you maintain optimal performance and ensures your environment meets the necessary infrastructure specifications. By confirming these requirements upfront, you can avoid potential deployment delays and ensure a smooth setup process.

Once you have provided all the required details, you will be prompted to specify your host details. These are the underlying Amazon EC2 instances that your VCF environment will get deployed in.

Once you have filled out details for each of your host instances, you will need to configure your networking and management appliance DNS details. For further information on how to create a new VCF environment on Amazon EVS, follow the documentation here.

After you have created your VCF environment, you will be able to look over all of the host and configuration details through the AWS Console.

Additional things to know

Amazon EVS currently supports VCF version 5.2.1 and runs on i4i.metal instances. Future releases will expand VCF versions, licensing options, and more instance type support to provide even more flexibility for your deployments.

Amazon EVS provides flexible storage options. Your Amazon EVS local Instance storage is powered by VMware’s vSAN solution, which pools local disks across multiple ESXi hosts into a single distributed datastore. To scale your storage, you can leverage external Network File System (NFS) or iSCSI-based storage solutions. For example, Amazon FSx for NetApp ONTAP is particularly well-suited for use as an NFS datastore or shared block storage over iSCSI.

Additionally, Amazon EVS makes connecting your on-premises environments to AWS simple. You can connect from on-premises vSphere environment into Amazon EVS using a Direct Connect connection or a VPN that terminates into a transit gateway. Amazon EVS also manages the underlying connectivity from your VLAN subnets into your VMs.

AWS provides comprehensive support for all AWS services deployed by Amazon EVS, handling direct customer support while engaging with Broadcom for advanced support needs. Customers must maintain AWS Business Support on accounts running the service.

Availability and pricing

Amazon EVS is now generally available in US East (N. Virginia), US East (Ohio), US West (Oregon), Europe (Frankfurt), Europe (Ireland), and Asia Pacific (Tokyo) AWS Regions, with additional Regions coming soon. Pricing is based on the Amazon EC2 instances and AWS resources you use, with no minimum fees or upfront commitments.

To learn more, visit the Amazon EVS product page.

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As a developer advocate at AWS, I’ve worked with many enterprise organizations who operate critical applications across multiple AWS Regions. A key concern they often share is the lack of confidence in their Region failover strategy—whether it will work when needed, whether all dependencies have been identified, and whether their teams have practiced the procedures enough. Traditional approaches often leave them uncertain about their readiness for Regional switch.

Today, I’m excited to announce Amazon Application Recovery Controller (ARC) Region switch, a fully managed, highly available capability that enables organizations to plan, practice, and orchestrate Region switches with confidence, eliminating the uncertainty around cross-Region recovery operations. Region switch helps you orchestrate recovery for your multi-Region applications on AWS. It gives you a centralized solution to coordinate and automate recovery tasks across AWS services and accounts when you need to switch your application’s operations from one AWS Region to another.

Many customers deploy business-critical applications across multiple AWS Regions to meet their availability requirements. When an operational event impacts an application in one Region, switching operations to another Region involves coordinating multiple steps across different AWS services, such as compute, databases, and DNS. This coordination typically requires building and maintaining complex scripts that need regular testing and updates as applications evolve. Additionally, orchestrating and tracking the progress of Region switches across multiple applications and providing evidence of successful recovery for compliance purposes often involves manual data gathering.

Region switch is built on a Regional data plane architecture, where Region switch plans are executed from the Region being activated. This design eliminates dependencies on the impacted Region during the switch, providing a more resilient recovery process since the execution is independent of the Region you’re switching from.

Building a recovery plan with ARC Region switch
With ARC Region switch, you can create recovery plans that define the specific steps needed to switch your application between Regions. Each plan contains execution blocks that represent actions on AWS resources. At launch, Region switch supports nine types of execution blocks:

• ARC Region switch plan execution block–let you orchestrate the order in which multiple applications switch to the Region you want to activate by referencing other Region switch plans.
• Amazon EC2 Auto Scaling execution block–Scales Amazon EC2 compute resources in your target Region by matching a specified percentage of your source Region’s capacity.
• ARC routing controls execution block–Changes routing control states to redirect traffic using DNS health checks.
• Amazon Aurora global database execution block–Performs database failover with potential data loss or switchover with zero data loss for Aurora Global Database.
• Manual approval execution block–Adds approval checkpoints in your recovery workflow where team members can review and approve before proceeding.
• Custom Action AWS Lambda execution block–Adds custom recovery steps by executing Lambda functions in either the activating or deactivating Region.
• Amazon Route 53 health check execution block–Let you to specify which Regions your application’s traffic will be redirected to during failover. When executing your Region switch plan, the Amazon Route 53 health check state is updated and traffic is redirected based on your DNS configuration.
• Amazon Elastic Kubernetes Service (Amazon EKS) resource scaling execution block–Scales Kubernetes pods in your target Region during recovery by matching a specified percentage of your source Region’s capacity.
• Amazon Elastic Container Service (Amazon ECS) resource scaling execution block–Scales ECS tasks in your target Region by matching a specified percentage of your source Region’s capacity.

Region switch continually validates your plans by checking resource configurations and AWS Identity and Access Management (IAM) permissions every 30 minutes. During execution, Region switch monitors the progress of each step and provides detailed logs. You can view execution status through the Region switch dashboard and at the bottom of the execution details page.

To help you balance cost and reliability, Region switch offers flexibility in how you prepare your standby resources. You can configure the desired percentage of compute capacity to target in your destination Region during recovery using Region switch scaling execution blocks. For critical applications expecting surge traffic during recovery, you might choose to scale beyond 100 percent capacity, and setting a lower percentage can help achieve faster overall execution times. However, it’s important to note that using one of the scaling execution blocks does not guarantee capacity, and actual resource availability depends on the capacity in the destination Region at the time of recovery. To facilitate the best possible outcomes, we recommend regularly testing your recovery plans and maintaining appropriate Service Quotas in your standby Regions.

ARC Region switch includes a global dashboard you can use to monitor the status of Region switch plans across your enterprise and Regions. Additionally, there’s a Regional executions dashboard that only displays executions within the current console Region. This dashboard is designed to be highly available across each Region so it can be used during operational events.

Region switch allows resources to be hosted in an account that is separate from the account that contains the Region switch plan. If the plan uses resources from an account that is different from the account that hosts the plan, then Region switch uses the executionRole to assume the crossAccountRole to access those resources. Additionally, Region switch plans can be centralized and shared across multiple accounts using AWS Resource Access Manager (AWS RAM), enabling efficient management of recovery plans across your organization.

Let’s see how it works
Let me show you how to create and execute a Region switch plan. There are three parts in this demo. First, I create a Region switch plan. Then, I define a workflow. Finally, I configure the triggers.

Step 1: Create a plan

I navigate to the Application Recovery Controller section of the AWS Management Console. I choose Region switch in the left navigation menu. Then, I choose Create Region switch plan.

After I give a name to my plan, I specify a Multi-Region recovery approach (active/passive or active/active). In Active/Passive mode, two application replicas are deployed into two Regions, with traffic routed into the active Region only. The replica in the passive Region can be activated by executing the Region switch plan.

Then, I select the Primary Region and Standby Region. Optionally, I can enter a Desired recovery time objective (RTO). The service will use this value to provide insight into how long Region switch plan executions take in relation to my desired RTO.

I enter the Plan execution IAM role. This is the role that allows Region switch to call AWS services during execution. I make sure the role I choose has permissions to be invoked by the service and contains the minimum set of permissions allowing ARC to operate. Refer to the IAM permissions section of the documentation for the details.

Step 2: Create a workflow

When the two Plan evaluation status notifications are green, I create a workflow. I choose Build workflows to get started.

Plans enable you to build specific workflows that will recover your applications using Region switch execution blocks. You can build workflows with execution blocks that run sequentially or in parallel to orchestrate the order in which multiple applications or resources recover into the activating Region. A plan is made up of these workflows that allow you to activate or deactivate a specific Region.

For this demo, I use the graphical editor to create the workflow. But you can also define the workflow in JSON. This format is better suited for automation or when you want to store your workflow definition in a source code management system (SCMS) and your infrastructure as code (IaC) tools, such as AWS CloudFormation.

I can alternate between the Design and the Code views by selecting the corresponding tab next to the Workflow builder title. The JSON view is read-only. I designed the workflow with the graphical editor and I copied the JSON equivalent to store it alongside my IaC project files.

Region switch launches an evaluation to validate your recovery strategy every 30 minutes. It regularly checks that all actions defined in your workflows will succeed when executed. This proactive validation assesses various elements, including IAM permissions and resource states across accounts and Regions. By continually monitoring these dependencies, Region switch helps ensure your recovery plans remain viable and identifies potential issues before they impact your actual switch operations.

However, just as an untested backup is not a reliable backup, an untested recovery plan cannot be considered truly validated. While continuous evaluation provides a strong foundation, we strongly recommend regularly executing your plans in test scenarios to verify their effectiveness, understand actual recovery times, and ensure your teams are familiar with the recovery procedures. This hands-on testing is essential for maintaining confidence in your disaster recovery strategy.

Step 3: Create a trigger

A trigger defines the conditions to activate the workflows just created. It’s expressed as a set of CloudWatch alarms. Alarm-based triggers are optional. You can also use Region switch with manual triggers.

From the Region switch page in the console, I choose the Triggers tab and choose Add triggers.

For each Region defined in my plan, I choose Add trigger to define the triggers that will activate the Region.Finally, I choose the alarms and their state (OK or Alarm) that Region switch will use to trigger the activation of the Region.

I’m now ready to test the execution of the plan to switch Regions using Region switch. It’s important to execute the plan from the Region I’m activating (the target Region of the workflow) and use the data plane in that specific Region.

Here is how to execute a plan using the AWS Command Line Interface (AWS CLI):

aws arc-region-switch start-plan-execution 
--plan-arn arn:aws:arc-region-switch::111122223333:plan/resource-id 
--target-region us-west-2 
--action activate

Pricing and availability
Region switch is available in all commercial AWS Regions at $70 per month per plan. Each plan can include up to 100 execution blocks, or you can create parent plans to orchestrate up to 25 child plans.

Having seen firsthand the engineering effort that goes into building and maintaining multi-Region recovery solutions, I’m thrilled to see how Region switch will help automate this process for our customers. To get started with ARC Region switch, visit the ARC console and create your first Region switch plan. For more information about Region switch, visit the Amazon Application Recovery Controller (ARC) documentation. You can also reach out to your AWS account team with questions about using Region switch for your multi-Region applications.

I look forward to hearing about how you use Region switch to strengthen your multi-Region applications’ resilience.

— seb

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Today, we’re announcing the general availability of Amazon DocumentDB Serverless, a new configuration for Amazon DocumentDB (with MongoDB compatibility) that automatically scales compute and memory based on your application’s demand. Amazon DocumentDB Serverless simplifies database management with no upfront commitments or additional costs, offering up to 90 percent cost savings compared to provisioning for peak capacity.

With Amazon DocumentDB Serverless, you can use the same MongoDB compatible-APIs and capabilities as Amazon DocumentDB, including read replicas, Performance Insights, I/O optimized, and integrations with other Amazon Web Services (AWS) services.

Amazon DocumentDB Serverless introduces a new database configuration measured in a DocumentDB Capacity Unit (DCU), a combination of approximately 2 gibibytes (GiB) of memory, corresponding CPU, and networking. It continually tracks utilization of resources such as CPU, memory, and network coming from database operations performed by your application.

Amazon DocumentDB Serverless automatically scales DCUs up or down to meet demand without disrupting database availability. Switching from provisioned instances to serverless in an existing cluster is as straightforward as adding or changing the instance type. This transition doesn’t require any data migration. To learn more, visit How Amazon DocumentDB Serverless works.

Some key use cases and advantages of Amazon DocumentDB Serverless include:

• Variable workloads – With Amazon DocumentDB Serverless, you can handle sudden traffic spikes such as periodic promotional events, development and testing environments, and new applications where usage might ramp up quickly. You can also build agentic AI applications that benefit from built-in vector search for Amazon DocumentDB and serverless adaptability to handle dynamically invoked agentic AI workflows.
• Multi-tenant workloads – You can use Amazon DocumentDB Serverless to manage individual database capacity across the entire database fleet. You don’t need to manage hundreds or thousands of databases for enterprises applications or multi-tenant environments of a software as a service (SaaS) vendor.
• Mixed-use workloads – You can balance read and write capacity in workloads that periodically experience spikes in query traffic, such as online transaction processing (OLTP) applications. By specifying promotion tiers for Amazon DocumentDB Serverless instances in a cluster, you can configure your cluster so that the reader instances can scale independently of the writer instance to handle the additional load.

For steady workloads, Amazon DocumentDB provisioned instances are more suitable. You can select an instance class that offers a predefined amount of memory, CPU power, and I/O bandwidth. If your workload changes when using provisioned instances, you should manually modify the instance class of your writer and readers. Optionally, you can add serverless instances to an existing provisioned Amazon DocumentDB cluster at any time.

Amazon DocumentDB Serverless in action
To get started with Amazon DocumentDB Serverless, go to the Amazon DocumentDB console. In the left navigation pane, choose Clusters and Create.

On the Create Amazon DocumentDB cluster page, choose Instance-based cluster type and then Serverless instance configuration. You can choose minimum and maximum capacity DCUs. Amazon DocumentDB Serverless is supported starting with Amazon DocumentDB 5.0.0 and higher with a capacity range of 0.5–256 DCUs.

If you use features such as auditing and Performance Insights, consider adding DCUs for each feature. To learn more, visit Amazon DocumentDB Serverless scaling configuration.

To add a serverless instance to an existing provisioned cluster, choose Add instances on the Actions menu when you choose the provisioned cluster. If you use a cluster with an earlier version such as 3.6 or 4.0, you should first upgrade the cluster to the supported engine version (5.0).

On the Add instances page, choose Serverless in the DB instance class section for each new serverless instance you want to create. To add another instance, choose Add instance and continue adding instances until you have reached the desired number of new instances. Choose Create.

You can perform a failover operation to make a DocumentDB Serverless instance the cluster writer. Also, you can convert any remaining provisioned Amazon DocumentDB instances to DocumentDB Serverless instances by changing an instance’s class or removing them from the cluster by deleting an Amazon DocumentDB instance.

Now, you can connect to your Amazon DocumentDB cluster using AWS CloudShell. Choose Connect to cluster, and you can see the AWS CloudShell Run command screen. Enter a unique name in New environment name and choose Create and run.

When prompted, enter the password for the Amazon DocumentDB cluster. You’re successfully connected to your Amazon DocumentDB cluster, and you can run a few queries to get familiar with using a document database.

To learn more, visit Creating a cluster that uses Amazon DocumentDB Serverless and Managing Amazon DocumentDB Serverless in the AWS documentation.

Now available
Amazon DocumentDB Serverless is now available starting with Amazon DocumentDB 5.0 for both new and existing clusters. You only pay a flat rate per second of DCU usage. To learn more about pricing details and Regional availability, visit the Amazon DocumentDB pricing page.

Give these new features a try in the Amazon DocumentDB console and send feedback to AWS re:Post for Amazon DocumentDB or through your usual AWS Support contacts.

— Channy

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I’m writing this as I depart from Ho Chi Minh City back to Singapore. Just realized what a week it’s been, so let me rewind a bit. This week, I tried my first Corne keyboard, wrapped up rehearsals for AWS Summit Jakarta with speakers who are absolutely raising the bar, and visited Vietnam to participate as a technical keynote speaker in AWS Community Day Vietnam, an energetic gathering of hundreds of cloud practitioners and AWS enthusiasts who shared knowledge through multiple technical tracks and networking sessions.

What I presented was a keynote titled “Reinvent perspective as modern developers”, featuring serverless, containers, and how we can cut the learning curves and be more productive with Amazon Q Developer and Kiro. I got a chance to discuss with a couple of AWS Community Builders and community developers, who shared how Amazon Q Developer actually addressed their challenges on building applications, with several highlighting significant productivity improvements and smoother learning curves in their cloud development journeys.

As I head back to Singapore, I’m carrying with me not just memories of delicious cà phê sữa đá (iced milk coffee), but also fresh perspectives and inspirations from this vibrant community of cloud innovators.

Introducing Kiro
One of the highlights from last week was definitely Kiro, an AI IDE that helps you deliver from concept to production through a simplified developer experience for working with AI agents. Kiro goes beyond “vibe coding” with features like specs and hooks that help get prototypes into production systems with proper planning and clarity.

Join the waitlist to get notified when it becomes available.

Last week’s AWS Launches
In other news, last week we had AWS Summit in New York, where we released several services. Here are some launches that caught my attention:

• Simplify serverless development with console to IDE and remote debugging for AWS Lambda — AWS Lambda now offers console to IDE integration and remote debugging capabilities that streamline the developer workflow from browser to Visual Studio Code. These enhancements eliminate time-consuming context switching and enable developers to debug Lambda functions directly in their preferred IDE environment.

• Accelerate safe software releases with new built-in blue/green deployments in Amazon ECS — Amazon ECS now provides built-in blue-green deployment capability that makes containerized application deployments safer and more consistent. This eliminates the need to build custom deployment tooling while giving you confidence to ship software updates with rollback capability and deployment lifecycle hooks.

• Introducing Amazon Bedrock AgentCore: Securely deploy and operate AI agents at any scale — Amazon Bedrock AgentCore is a comprehensive set of enterprise-grade services that help developers quickly and securely deploy AI agents at scale using any framework and model. It includes AgentCore Runtime, Memory, Observability, Identity, Gateway, Browser, and Code Interpreter services that work together to eliminate infrastructure complexity.
• AWS Free Tier update: New customers can get started and explore AWS with up to $200 in credits — AWS Free Tier now offers enhanced benefits with up to $200 in AWS credits for new customers. You receive $100 upon sign-up and can earn an additional $100 by completing activities with EC2, RDS, Lambda, Bedrock, and AWS Budgets, making it easier to explore AWS services without incurring costs.

• Monitor and debug event-driven applications with new Amazon EventBridge logging — Amazon EventBridge now provides enhanced logging capabilities that offer comprehensive event lifecycle tracking with detailed information about successes, failures, and status codes. This new observability feature addresses microservices and event-driven architecture monitoring challenges by providing visibility into the complete event journey.

• Introducing Amazon S3 Vectors: First cloud storage with native vector support at scale — Amazon S3 Vectors is a purpose-built durable vector storage solution that can reduce the total cost of uploading, storing, and querying vectors by up to 90%. It’s the first cloud object store with native support to store large vector datasets and provide subsecond query performance for AI applications.

• Amazon EKS enables ultra-scale AI/ML workloads with support for 100k nodes per cluster — Amazon EKS now supports up to 100,000 worker nodes in a single cluster, enabling customers to scale up to 1.6 million AWS Trainium accelerators or 800K NVIDIA GPUs. This industry-leading scale empowers customers to train trillion-parameter models and advance AGI development while maintaining Kubernetes conformance and familiar developer experience.

From AWS Builder Center
In case you missed it, we just launched AWS Builder Center and integrated community.aws. Here are my top picks from the posts:

• How I Optimized My AWS Bill by Deleting My Account by Corey Quinn — A humorous yet insightful take on AWS cost optimization strategies and the extreme measures some might consider for bill reduction.
• How to setup MCP with UV in Python the right way by Du’An Lightfoot — A practical guide on setting up Model Context Protocol (MCP) with UV package manager in Python for optimal development workflow.
• Extending My Blog with Translations by Amazon Nova by Jimmy Dahlqvist — Learn how to leverage Amazon Nova’s capabilities to add translation features to your blog and reach a global audience.
• How I used Amazon Q CLI to fix Amazon Q CLI error “Amazon Q is having trouble responding right now” by Matias Kreder — A practical troubleshooting guide that demonstrates using Amazon Q CLI to resolve its own errors, showcasing the power of AI-assisted debugging.

Upcoming AWS events
Check your calendars and sign up for upcoming AWS and AWS Community events:

• AWS re:Invent – Register now to get a head start on choosing your best learning path, booking travel and accommodations, and bringing your team to learn, connect, and have fun. If you’re an early-career professional, you can apply to the All Builders Welcome Grant program, which is designed to remove financial barriers and create diverse pathways into cloud technology.
• AWS Builders Online Series – If you’re based in one of the Asia Pacific time zones, join and learn fundamental AWS concepts, architectural best practices, and hands-on demonstrations to help you build, migrate, and deploy your workloads on AWS.
• AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Taipei (July 29), Mexico City (August 6), and Jakarta (June 26–27).
• AWS Community Days — Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Singapore (August 2), Australia (August 15), Adria (September 5), Baltic (September 10), and Aotearoa (September 18).

You can browse all upcoming AWS led in-person and virtual developer-focused events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Donnie

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

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Join Builder ID: Get started with your AWS Builder journey at builder.aws.com

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Today, we’re announcing two significant enhancements to AWS Lambda that make it easier than ever for developers to build and debug serverless applications in their local development environments: console to IDE integration and remote debugging. These new capabilities build upon our recent improvements to the Lambda development experience, including the enhanced in-console editing experience and the improved local integrated development environment (IDE) experience launched in late 2024.

When building serverless applications, developers typically focus on two areas to streamline their workflow: local development environment setup and cloud debugging capabilities. While developers can bring functions from the console to their IDE, they’re looking for ways to make this process more efficient. Additionally, as functions interact with various AWS services in the cloud, developers want enhanced debugging capabilities to identify and resolve issues earlier in the development cycle, reducing their reliance on local emulation and helping them optimize their development workflow.

Console to IDE integration

To address the first challenge, we’re introducing console to IDE integration, which streamlines the workflow from the AWS Management Console to Visual Studio Code (VS Code). This new capability adds an Open in Visual Studio Code button to the Lambda console, enabling developers to quickly move from viewing their function in the browser to editing it in their IDE, eliminating the time-consuming setup process for local development environments.

The console to IDE integration automatically handles the setup process, checking for VS Code installation and the AWS Toolkit for VS Code. For developers that have everything already configured, choosing the button immediately opens their function code in VS Code, so they can continue editing and deploy changes back to Lambda in seconds. If VS Code isn’t installed, it directs developers to the download page, and if the AWS Toolkit is missing, it prompts for installation.

To use console to IDE, look for the Open in VS Code button in either the Getting Started popup after creating a new function or the Code tab of existing Lambda functions. After selecting, VS Code opens automatically (installing AWS Toolkit if needed). Unlike the console environment, you now have access to a full development environment with integrated terminal – a significant improvement for developers who need to manage packages (npm install, pip install), run tests, or use development tools like linters and formatters. You can edit code, add new files/folders, and any changes you make will trigger an automatic deploy prompt. When you choose to deploy, the AWS Toolkit automatically deploys your function to your AWS account.

Remote debugging

Once developers have their functions in their IDE, they can use remote debugging to debug Lambda functions deployed in their AWS account directly from VS Code. The key benefit of remote debugging is that it allows developers to debug functions running in the cloud while integrated with other AWS services, enabling faster and more reliable development.

With remote debugging, developers can debug their functions with complete access to Amazon Virtual Private Cloud (VPC) resources and AWS Identity and Access Management (AWS IAM) roles, eliminating the gap between local development and cloud execution. For example, when debugging a Lambda function that interacts with an Amazon Relational Database Service (Amazon RDS) database in a VPC, developers can now debug the execution environment of the function running in the cloud within seconds, rather than spending time setting up a local environment that might not match production.

Getting started with remote debugging is straightforward. Developers can select a Lambda function in VS Code and enable debugging in seconds. AWS Toolkit for VS Code automatically downloads the function code, establishes a secure debugging connection, and enables breakpoint setting. When debugging is complete, AWS Toolkit for VS Code automatically cleans up the debugging configuration to prevent any impact on production traffic.

Let’s try it out

To take remote debugging for a spin, I chose to start with a basic “hello world” example function, written in Python. I had previously created the function using the AWS Management Console for AWS Lambda. Using the AWS Toolkit for VS Code, I can navigate to my function in the Explorer pane. Hovering over my function, I can right-click (ctrl-click in Windows) to download the code to my local machine to edit the code in my IDE. Saving the file will ask me to decide if I want to deploy the latest changes to Lambda.

From here, I can select the play icon to open the Remote invoke configuration page for my function. This dialog will now display a Remote debugging option, which I configure to point at my local copy of my function handler code. Before choosing Remote invoke, I can set breakpoints on the left anywhere I want my code to pause for inspection.

My code will be running in the cloud after it’s invoked, and I can monitor its status in real time in VS Code. In the following screenshot, you can see I’ve set a breakpoint at the print statement. My function will pause execution at this point in my code, and I can inspect things like local variable values before either continuing to the next breakpoint or stepping into the code line by line.

Here, you can see that I’ve chosen to step into the code, and as I go through it line by line, I can see the context and local and global variables displayed on the left side of the IDE. Additionally, I can follow the logs in the Output tab at the bottom of the IDE. As I step through, I’ll see any log messages or output messages from the execution of my function in real time.

Enhanced development workflow

These new capabilities work together to create a more streamlined development experience. Developers can start in the console, quickly transition to VS Code using the console to IDE integration, and then use remote debugging to debug their functions running in the cloud. This workflow eliminates the need to switch between multiple tools and environments, helping developers identify and fix issues faster.

Now available

You can start using these new features through the AWS Management Console and VS Code with the AWS Toolkit for VS Code (v3.69.0 or later) installed. Console to IDE integration is available in all commercial AWS Regions where Lambda is available, except AWS GovCloud (US) Regions. Learn more about it in Lambda and AWS Toolkit for VS Code documentation. To learn more about remote debugging capability, including AWS Regions it is available in, visit the AWS Toolkit for VS Code and Lambda documentation.

Console to IDE and remote debugging are available to you at no additional cost. With remote debugging, you pay only for the standard Lambda execution costs during debugging sessions. Remote debugging will support Python, Node.js, and Java runtimes at launch, with plans to expand support to additional runtimes in the future.

These enhancements represent a significant step forward in simplifying the serverless development experience, which means developers can build and debug Lambda functions more efficiently than ever before.

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Since 2018, AWS DeepRacer has engaged over 560,000 builders worldwide, demonstrating that developers learn and grow through competitive experiences. Today, we’re excited to expand into the generative AI era with AWS Artificial Intelligence (AI) League.

This is a unique competitive experience – your chance to dive deep into generative AI regardless of your skill level, compete with peers, and build solutions that solve actual business problems through an engaging, competitive experience.

With AWS AI League, your organization hosts private tournaments where teams collaborate and compete to solve real-world business use cases using practical AI skills. Participants craft effective prompts and fine-tune models while building powerful generative AI solutions relevant for their business. Throughout the competition, participants’ solutions are evaluated against reference standards on a real-time leaderboard that tracks performance based on accuracy and latency.

The AWS AI League experience starts with a 2-hour hands-on workshop led by AWS experts. This is followed by self-paced experimentation, culminating in a gameshow-style grand finale where participants showcase their generative AI creations addressing business challenges. Organizations can set up their own AWS AI League within half a day. The scalable design supports 500 to 5,000 employees while maintaining the same efficient timeline.

Supported by up to $2 million in AWS credits and a $25,000 championship prize pool at AWS re:Invent 2025, the program provides a unique opportunity to solve real business challenges.

AWS AI League transforms how organizations develop generative AI capabilities
AWS AI League transforms how organizations develop generative AI capabilities by combining hands-on skills development, domain expertise, and gamification. This approach makes AI learning accessible and engaging for all skill levels. Teams collaborate through industry-specific challenges that mirror real organizational needs, with each challenge providing reference datasets and evaluation standards that reflect actual business requirements.

• Customizable industry-specific challenges – Tailor competitions to your specific business context. Healthcare teams work on patient discharge summaries, financial services focus on fraud detection, and media companies develop content creation solutions.
• Integrated AWS AI stack experience – Participants gain hands-on experience with AWS AI and ML tools, including Amazon SageMaker AI, Amazon Bedrock, and Amazon Nova, accessible from Amazon SageMaker Unified Studio. Teams work through a secure, cost-controlled environment within their organization’s AWS account.
• Real-time performance tracking – The leaderboard evaluates submissions against established benchmarks and reference standards throughout the competition, providing immediate feedback on accuracy and speed so teams can iterate and improve their solutions. During the final round, this scoring includes expert evaluation where domain experts and a live audience participate in real-time voting to determine which AI solutions best solve real business challenges.

• AWS AI League offers two foundational competition tracks:
  • Prompt Sage – The Ultimate Prompt Battle – Race to craft the perfect AI prompts that unlock breakthrough solutions. whether you detect financial fraud or streamlining healthcare workflows, every word counts as they climb the leaderboard using zero-shot learning and chain-of-thought reasoning.
  • Tune Whiz – The Model Mastery Showdown – Generic AI models meet their match as you sculpt them into industry-specific powerhouses. Armed with your domain expertise and specialized questions, competitors fine-tune models that speak your business language fluently. Victory goes to who achieve the perfect balance of blazing performance, lightning efficiency, and cost optimization.

As Generative AI continues to evolve, AWS AI League will regularly introduce new challenges and formats in addition to these tracks.

Get started today
Ready to get started? Organizations can host private competitions by applying through the AWS AI League page. Individual developers can join public competitions at AWS Summits and AWS re:Invent.

PS: Writing a blog post at AWS is always a team effort, even when you see only one name under the post title. In this case, I want to thank Natasya Idries, for her generous help with technical guidance, and expertise, which made this overview possible and comprehensive.

— Eli