Best performance and fastest memory with the new Amazon EC2 R8i and R8i-flex instances

Today, we’re announcing general availability of the new eighth generation, memory optimized Amazon Elastic Compute Cloud (Amazon EC2) R8i and R8i-flex instances powered by custom Intel Xeon 6 processors, available only on AWS. They deliver the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. These instances deliver up to 15 percent better price performance, 20 percent higher performance, and 2.5 times more memory throughput compared to previous generation instances.

With these improvements, R8i and R8i-flex instances are ideal for a variety of memory intensive workloads such as SQL and NoSQL databases, distributed web scale in-memory caches (Memcached and Redis), in-memory databases such as SAP HANA, and real-time big data analytics (Apache Hadoop and Apache Spark clusters). For a majority of the workloads that don’t fully utilize the compute resources, the R8i-flex instances are a great first choice to achieve an additional 5 percent better price performance and 5 percent lower prices.

Improvements made to both instances compared to their predecessors
In terms of performance, R8i and R8i-flex instances offer 20 percent better performance than R7i instances, with even higher gains for specific workloads. These instances are up to 30 percent faster for PostgreSQL databases, up to 60 percent faster for NGINX web applications, and up to 40 percent faster for AI deep learning recommendation models compared to previous generation R7i instances, with sustained all-core turbo frequency now reaching 3.9 GHz (compared to 3.2 GHz in the previous generation). They also feature a 4.6x larger L3 cache and significantly better memory throughput, offering 2.5 times higher memory bandwidth than the seventh generation. With this higher performance across all the vectors, you can run a greater number of workloads while keeping costs down.

R8i instances now scale up to 96xlarge with up to 384 vCPUs and 3TB memory (versus 48xlarge sizes in the seventh generation), helping you to scale up database applications. R8i instances are SAP certified to deliver 142,100 aSAPS, which is highest among all comparable machines in on premises and cloud environments, delivering exceptional performance for your mission-critical SAP workloads. R8i-flex instances offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources. Both R8i and R8i-flex instances use the latest sixth generation AWS Nitro Cards, delivering up to two times more network and Amazon Elastic Block Storage (Amazon EBS) bandwidth compared to the previous generation, which greatly improves network throughput for workloads handling small packets, such as web, application, and gaming servers.

R8i and R8i-flex instances also support bandwidth configuration with 25 percent allocation adjustments between network and Amazon EBS bandwidth, enabling better database performance, query processing, and logging speeds. Additional enhancements include FP16 datatype support for Intel AMX to support workloads such as deep learning training and inference and other artificial intelligence and machine learning (AI/ML) applications.

The specs for the R8i instances are as follows.

Instance size	vCPUs	Memory (GiB)	Network bandwidth (Gbps)	EBS bandwidth (Gbps)
r8i.large	2	16	Up to 12.5	Up to 10
r8i.xlarge	4	32	Up to 12.5	Up to 10
r8i.2xlarge	8	64	Up to 15	Up to 10
r8i.4xlarge	16	128	Up to 15	Up to 10
r8i.8xlarge	32	256	15	10
r8i.12xlarge	48	384	22.5	15
r8i.16xlarge	64	512	30	20
r8i.24xlarge	96	768	40	30
r8i.32xlarge	128	1024	50	40
r8i.48xlarge	192	1536	75	60
r8i.96xlarge	384	3072	100	80
r8i.metal-48xl	192	1536	75	60
r8i.metal-96xl	384	3072	100	80

The specs for the R8i-flex instances are as follows.

Instance size	vCPUs	Memory (GiB)	Network bandwidth (Gbps)	EBS bandwidth (Gbps)
r8i-flex.large	2	16	Up to 12.5	Up to 10
r8i-flex.xlarge	4	32	Up to 12.5	Up to 10
r8i-flex.2xlarge	8	64	Up to 15	Up to 10
r8i-flex.4xlarge	16	128	Up to 15	Up to 10
r8i-flex.8xlarge	32	256	Up to 15	Up to 10
r8i-flex.12xlarge	48	384	Up to 22.5	Up to 15
r8i-flex.16xlarge	64	512	Up to 30	Up to 20

When to use the R8i-flex instances
As stated earlier, R8i-flex instances are more affordable versions of the R8i instances, offering up to 5 percent better price performance at 5 percent lower prices. They’re designed for workloads that benefit from the latest generation performance but don’t fully use all compute resources. These instances can reach up to the full CPU performance 95 percent of the time and work well for in-memory databases, distributed web scale cache stores, mid-size in-memory analytics, real-time big data analytics, and other enterprise applications. R8i instances are recommended for more demanding workloads that need sustained high CPU, network, or EBS performance such as analytics, databases, enterprise applications, and web scale in-memory caches.

Available now
R8i and R8i-flex instances are available today in the US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Spain) AWS Regions. As usual with Amazon EC2, you pay only for what you use. For more information, refer to Amazon EC2 Pricing. Check out the full collection of memory optimized instances to help you start migrating your applications.

To learn more, visit our Amazon EC2 R8i instances page and Amazon EC2 R8i-flex instances page. Send feedback to AWS re:Post for EC2 or through your usual AWS Support contacts.

– Veliswa

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Shipment Invoicing and Orders APIs to provide new payment data for the Brazil marketplace

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AWS Weekly Roundup: Single GPU P5 instances, Advanced Go Driver, Amazon SageMaker HyperPod and more (August 18, 2025)

Let me start this week’s update with something I’m especially excited about – the upcoming BeSA (Become a Solutions Architect) cohort. BeSA is a free mentoring program that I host along with a few other AWS employees on a volunteer basis to help people excel in their cloud careers. Last week, the instructors’ lineup was finalized for the 6-week cohort starting September 6. The cohort will focus on migration and modernization on AWS. Visit the BeSA website to learn more.

Another highlight for me last week was the announcement of six new AWS Heroes for their technical leadership and exceptional contributions to the AWS community. Read the full announcement to learn more about these community leaders.

Last week’s launches
Here are some launches from last week that got my attention:

• Amazon EC2 Single GPU P5 instances are now generally available — You can right-size your machine learning (ML) and high performance computing (HPC) resources cost-effectively with the new Amazon Elastic Compute Cloud (Amazon EC2) P5 instance size with one NVIDIA H100 GPU.
• AWS Advanced Go Driver is generally available — You can now use the AWS Advanced Go Driver with Amazon Relational Database Service (Amazon RDS) and Amazon Aurora PostgreSQL-Compatible and MySQL-Compatible database clusters for faster switchover and failover times, Federated Authentication, and authentication with AWS Secrets Manager or AWS Identity and Access Management (IAM). You can install the PostgreSQL and MySQL packages for Windows, Mac, or Linux, by following the installation guides in GitHub.
• Expanded support for Cilium with Amazon EKS Hybrid Nodes — Cilium is a Cloud Native Computing Foundation (CNCF) graduated project that provides core networking capabilities for Kubernetes workloads. Now, you can receive support from AWS for a broader set of Cilium features when using Cilium with Amazon EKS Hybrid Nodes including application ingress, in-cluster load balancing, Kubernetes network policies, and kube-proxy replacement mode.
• Amazon SageMaker AI now supports P6e-GB200 UltraServers — You can accelerate training and deployment of foundational models (FMs) at trillion-parameter scale by using up to 72 NVIDIA Blackwell GPUs under one NVLink domain with the new P6e-GB200 UltraServer support in Amazon SageMaker HyperPod and Model Training.
• Amazon SageMaker HyperPod now supports fine-grained quota allocation of compute resources, topology-aware-scheduling of LLM tasks and custom Amazon Machine Images (AMIs) — You can allocate fine-grained compute quota for GPU, Trainium accelerator, vCPU, and vCPU memory within an instance to optimize compute resource distribution. With topology-aware scheduling, you can schedule your large language model (LLM) tasks on an optimal network topology to minimize network communication and enhance training efficiency. Using custom AMIs, you can deploy clusters with pre-configured, security-hardened environments that meet your specific organizational requirements.

Additional updates
Here are some additional news items and blog posts that I found interesting:

• Celebrating 10 years of Amazon Aurora innovation — Join the livestream event on August 21, 2025, to celebrate a decade of Aurora database innovation.
• AWS named as a Leader in 2025 Gartner Magic Quadrant for Strategic Cloud Platform Services — For the fifteenth consecutive year, Gartner has named AWS a Leader in the Gartner Magic Quadrant for Strategic Cloud Platform Services (SCPS), making AWS the longest-running Magic Quadrant Leader.
• Introducing AWS Cloud Control API (CCAPI) MCP Server — You can now use natural language to managing cloud infrastructure using CCAPI MCP Server. You can create, read, update, delete, and list resources using natural language.
• Introducing Amazon Bedrock AgentCore Identity — AgentCore Identity provides a centralized capability to manage agent identities, securing credentials, and supporting seamless integration with AWS and third-party services through Sigv4, standardized OAuth 2.0 flows, and API key.
• Introducing Amazon Bedrock AgentCore Gateway — A fully managed service to connect AI agents with tools and services. It serves as a centralized tool server, providing a unified interface where agents can discover, access, and invoke tools.

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

• AWS re:Invent 2025 (December 1-5, 2025, Las Vegas) — The AWS flagship annual conference offering collaborative innovation through peer-to-peer learning, expert-led discussions, and invaluable networking opportunities.
• AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Coming up soon are summits in Johannesburg (August 20) and Toronto (September 4).
• 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: Adria (September 5), Baltic (September 10), Aotearoa (September 18), and South Africa (September 20).

Join the AWS Builder Center to learn, build, and connect with builders in the AWS community. Browse here for upcoming in-person and virtual developer-focused events.

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

– Prasad

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AWS named as a Leader in 2025 Gartner Magic Quadrant for Strategic Cloud Platform Services for 15 years in a row

On August 4, 2025, Gartner published its Gartner Magic Quadrant for Strategic Cloud Platform Services (SCPS). Amazon Web Services (AWS) is the longest-running Magic Quadrant Leader, with Gartner naming AWS a Leader for the fifteenth consecutive year.

In the report, Gartner once again placed AWS highest on the “Ability to Execute” axis. We believe this reflects our ongoing commitment to giving customers the broadest and deepest set of capabilities to accelerate innovation as well as unparalleled security, reliability, and performance they can trust for their most critical applications.

Here is the graphical representation of the 2025 Magic Quadrant for Strategic Cloud Platform Services.

Gartner recognized AWS strengths as:

• Largest cloud community – AWS has built a strong global community of cloud professionals, providing significant opportunities for learning and engagement.
• Cloud-inspired silicon – AWS has used its cloud computing experience to develop custom silicon designs, including AWS Graviton, AWS Inferentia, and AWS Trainium, which enable tighter integration between hardware and software, improved power efficiency, and greater control over supply chains.
• Global scale and operational execution – AWS’s significant share of global cloud market revenue has enabled it to build a larger and more robust network of integration partners than some other providers in this analysis, which in turn helps organizations successfully adopt cloud.

The most common feedback I hear from customers is that AWS has the largest and most dynamic cloud community, making it easy to ask questions and learn from millions of active customers and tens of thousands of partners globally. We recently launched our community hub, AWS Builder Center to connect directly with AWS Heroes and AWS Community Builders. You can also explore and join AWS User Groups and AWS Cloud Clubs in a city near you.

We have also focused on facilitating the digital transformation of enterprise customers through a number of enterprise programs, such as the AWS Migration Acceleration Program. Using generative AI on migration and modernization, we introduced AWS Transform, the first agentic AI service developed to accelerate enterprise modernization of mission-critical business workloads such as .NET, mainframe, and VMware.

Access the complete full Gartner report to learn more. It outlines the methodology and evaluation criteria used to develop their assessments of each cloud service provider included in the report. This report can serve as a guide when choosing a cloud provider that helps you innovate on behalf of your customers.

— Channy

Gartner does not endorse any vendor, product or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

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Celebrating 10 years of Amazon Aurora innovation

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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Meet our newest AWS Heroes — August 2025

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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AWS Weekly Roundup: OpenAI models, Automated Reasoning checks, Amazon EVS, and more (August 11, 2025)

AWS Summits in the northern hemisphere have mostly concluded but the fun and learning hasn’t yet stopped for those of us in other parts of the globe. The community, customers, partners, and colleagues enjoyed a day of learning and networking last week at the AWS Summit Mexico City and the AWS Summit Jakarta.

Last week’s launches
These are the launches from last week that caught my attention:

• OpenAI open weight models on AWS — OpenAI open weight models (gpt-oss-120b and gpt-oss-20b) are now available on AWS. These open weight models excel at coding, scientific analysis, and mathematical reasoning, with performance comparable to leading alternatives.
• Amazon Elastic VMware Service — 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), is now generally available.
• Automated Reasoning checks — Automated Reasoning checks, a new Amazon Bedrock Guardrails policy that was previewed during AWS re:Invent, is now generally available. Automated Reasoning checks helps you validate the accuracy of content generated by foundation models (FMs) against a domain knowledge. Read more in Danilo’s post on how this can help prevent factual errors that can be caused by AI hallucinations.
• Multi-Region application recovery service — In this post, Sébastien writes about the announcement of 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.

Additional updates
I thought these projects, blog posts, and news items were also interesting:

• Amazon Simple Queue Service (Amazon SQS) — Amazon SQS has increased the maximum message payload size from 256 KiB to 1 MiB, enabling customers to send and receive larger messages through their Amazon SQS standard and FIFO queues.
• AWS Lambda now supports GitHub Actions — AWS Lambda now enables you to use GitHub Actions to automatically deploy Lambda functions when you push code or configuration changes to your GitHub repository, streamlining your continuous integration and continuous deployment (CI/CD) pipeline for serverless applications.
• Console-to-Code on Amazon DynamoDB — Amazon DynamoDB announced the support of Console-to-Code, powered by Amazon Q Developer. Console-to-Code to make it simple, fast, and cost-effective to create DynamoDB resources at scale by getting you started with your automation code.
• You can dive deep into conversational AI with Amazon Lex by following this course built by AWS Hero Faye Ellis, available free as part of a free trial.
• AWS Community Builder Raphael Manke has published this year’s Unofficial AWS re:Invent Session Planner 2025. This planner is becoming highly awaited every year since it first launched a few years ago.
• AWS Hero Rosius Ndimofor built Educloud Academy and it’s heartwarming to see stories from community members who’re benefiting from this platform, such as this builder who’s participating in the latest Cloud and AI Challenge.

Upcoming AWS events
Keep a look out and be sure to sign up for these upcoming events:

AWS re:Invent 2025 (December 1-5, 2025, Las Vegas) — AWS’s flagship annual conference offering collaborative innovation through peer-to-peer learning, expert-led discussions, and invaluable networking opportunities.

AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Coming up soon are the summits at São Paulo (August 13) and Johannesburg (August 20).

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: Australia (August 15), Adria (September 5), Baltic (September 10), Aotearoa (September 18), and South Africa (September 20).

Join the AWS Builder Center to learn, build, and connect with builders in the AWS community. Browse here for upcoming in-person and virtual developer-focused events.

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

– Veliswa.

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Update: Battery compliance for Merchant Fulfillment Network (MFN)

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Minimize AI hallucinations and deliver up to 99% verification accuracy with Automated Reasoning checks: Now available

Today, I’m happy to share that Automated Reasoning checks, a new Amazon Bedrock Guardrails policy that we previewed during AWS re:Invent, is now generally available. Automated Reasoning checks helps you validate the accuracy of content generated by foundation models (FMs) against a domain knowledge. This can help prevent factual errors due to AI hallucinations. The policy uses mathematical logic and formal verification techniques to validate accuracy, providing definitive rules and parameters against which AI responses are checked for accuracy.

This approach is fundamentally different from probabilistic reasoning methods which deal with uncertainty by assigning probabilities to outcomes. In fact, Automated Reasoning checks delivers up to 99% verification accuracy, providing provable assurance in detecting AI hallucinations while also assisting with ambiguity detection when the output of a model is open to more than one interpretation.

With general availability, you get the following new features:

• Support for large documents in a single build, up to 80K tokens – Process extensive documentation; we found this can add up to 100 pages of content
• Simplified policy validation – Save your validation tests and run them repeatedly, making it easier to maintain and verify your policies over time
• Automated scenario generation – Create test scenarios automatically from your definitions, saving time and effort while helping make coverage more comprehensive
• Enhanced policy feedback – Provide natural language suggestions for policy changes, simplifying the way you can improve your policies
• Customizable validation settings – Adjust confidence score thresholds to match your specific needs, giving you more control over validation strictness

Let’s see how this works in practice.

Creating Automated Reasoning checks in Amazon Bedrock Guardrails
To use Automated Reasoning checks, you first encode rules from your knowledge domain into an Automated Reasoning policy, then use the policy to validate generated content. For this scenario, I’m going to create a mortgage approval policy to safeguard an AI assistant evaluating who can qualify for a mortgage. It is important that the predictions of the AI system do not deviate from the rules and guidelines established for mortgage approval. These rules and guidelines are captured in a policy document written in natural language.

In the Amazon Bedrock console, I choose Automated Reasoning from the navigation pane to create a policy.

I enter name and description of the policy and upload the PDF of the policy document. The name and description are just metadata and do not contribute in building the Automated Reasoning policy. I describe the source content to add context on how it should be translated into formal logic. For example, I explain how I plan to use the policy in my application, including sample Q&A from the AI assistant.

When the policy is ready, I land on the overview page, showing the policy details and a summary of the tests and definitions. I choose Definitions from the dropdown to examine the Automated Reasoning policy, made of rules, variables, and types that have been created to translate the natural language policy into formal logic.

The Rules describe how variables in the policy are related and are used when evaluating the generated content. For example, in this case, which are the thresholds to apply and how some of the decisions are taken. For traceability, each rule has its own unique ID.

The Variables represent the main concepts at play in the original natural language documents. Each variable is involved in one or more rules. Variables allow complex structures to be easier to understand. For this scenario, some of the rules need to look at the down payment or at the credit score.

Custom Types are created for variables that are neither boolean nor numeric. For example, for variables that can only assume a limited number of values. In this case, there are two type of mortgage described in the policy, insured and conventional.

Now we can assess the quality of the initial Automated Reasoning policy through testing. I choose Tests from the dropdown. Here I can manually enter a test, consisting of input (optional) and output, such as a question and its possible answer from the interaction of a customer with the AI assistant. I then set the expected result from the Automated Reasoning check. The expected result can be valid (the answer is correct), invalid (the answer is not correct), or satisfiable (the answer could be true or false depending on specific assumptions). I can also assign a confidence threshold for the translation of the query/content pair from natural language to logic.

Before I enter tests manually, I use the option to automatically generate a scenario from the definitions. This is the easiest way to validate a policy and (unless you’re an expert in logic) should be the first step after the creation of the policy.

For each generated scenario, I provide an expected validation to say if it is something that can happen (satisfiable) or not (invalid). If not, I can add an annotation that can then be used to update the definitions. For a more advanced understanding of the generated scenario, I can show the formal logic representation of a test using SMT-LIB syntax.

After using the generate scenario option, I enter a few tests manually. For these tests, I set different expected results: some are valid, because they follow the policy, some are invalid, because they flout the policy, and some are satisfiable, because their result depends on specific assumptions.

Then, I choose Validate all tests to see the results. All tests passed in this case. Now, when I update the policy, I can use these tests to validate that the changes didn’t introduce errors.

For each test, I can look at the findings. If a test doesn’t pass, I can look at the rules that created the contradiction that made the test fail and go against the expected result. Using this information, I can understand if I should add an annotation, to improve the policy, or correct the test.

Now that I’m satisfied with the tests, I can create a new Amazon Bedrock guardrail (or update an existing one) to use up to two Automated Reasoning policies to check the validity of the responses of the AI assistant. All six policies offered by Guardrails are modular, and can be used together or separately. For example, Automated Reasoning checks can be used with other safeguards such as content filtering and contextual grounding checks. The guardrail can be applied to models served by Amazon Bedrock or with any third-party model (such as OpenAI and Google Gemini) via the ApplyGuardrail API. I can also use the guardrail with an agent framework such as Strands Agents, including agents deployed using Amazon Bedrock AgentCore.

Now that we saw how to set up a policy, let’s look at how Automated Reasoning checks are used in practice.

Customer case study – Utility outage management systems
When the lights go out, every minute counts. That’s why utility companies are turning to AI solutions to improve their outage management systems. We collaborated on a solution in this space together with PwC. Using Automated Reasoning checks, utilities can streamline operations through:

• Automated protocol generation – Creates standardized procedures that meet regulatory requirements
• Real-time plan validation – Ensures response plans comply with established policies
• Structured workflow creation – Develops severity-based workflows with defined response targets

At its core, this solution combines intelligent policy management with optimized response protocols. Automated Reasoning checks are used to assess AI-generated responses. When a response is found to be invalid or satisfiable, the result of the Automated Reasoning check is used to rewrite or enhance the answer.

This approach demonstrates how AI can transform traditional utility operations, making them more efficient, reliable, and responsive to customer needs. By combining mathematical precision with practical requirements, this solution sets a new standard for outage management in the utility sector. The result is faster response times, improved accuracy, and better outcomes for both utilities and their customers.

In the words of Matt Wood, PwC’s Global and US Commercial Technology and Innovation Officer:

“At PwC, we’re helping clients move from AI pilot to production with confidence—especially in highly regulated industries where the cost of a misstep is measured in more than dollars. Our collaboration with AWS on Automated Reasoning checks is a breakthrough in responsible AI: mathematically assessed safeguards, now embedded directly into Amazon Bedrock Guardrails. We’re proud to be AWS’s launch collaborator, bringing this innovation to life across sectors like pharma, utilities, and cloud compliance—where trust isn’t a feature, it’s a requirement.”

Things to know
Automated Reasoning checks in Amazon Bedrock Guardrails is generally available today in the following AWS Regions: US East (Ohio, N. Virginia), US West (Oregon), and Europe (Frankfurt, Ireland, Paris).

With Automated Reasoning checks, you pay based on the amount of text processed. For more information, see Amazon Bedrock pricing.

To learn more, and build secure and safe AI applications, see the technical documentation and the GitHub code samples. Follow this link for direct access to the Amazon Bedrock console.

The videos in this playlist include an introduction to Automated Reasoning checks, a deep dive presentation, and hands-on tutorials to create, test, and refine a policy. This is the second video in the playlist, where my colleague Wale provides a nice intro to the capability.

— Danilo

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OpenAI open weight models now available on AWS

AWS is committed to bringing you the most advanced foundation models (FMs) in the industry, continuously expanding our selection to include groundbreaking models from leading AI innovators so that you always have access to the latest advancements to drive your business forward.

Today, I am happy to announce the availability of two new OpenAI models with open weights in Amazon Bedrock and Amazon SageMaker JumpStart. OpenAI gpt-oss-120b and gpt-oss-20b models are designed for text generation and reasoning tasks, offering developers and organizations new options to build AI applications with complete control over their infrastructure and data.

These open weight models excel at coding, scientific analysis, and mathematical reasoning, with performance comparable to leading alternatives. Both models support a 128K context window and provide adjustable reasoning levels (low/medium/high) to match your specific use case requirements. The models support external tools to enhance their capabilities and can be used in an agentic workflow, for example, using a framework like Strands Agents.

With Amazon Bedrock and Amazon SageMaker JumpStart, AWS gives you the freedom to innovate with access to hundreds of FMs from leading AI companies, including OpenAI open weight models. With our comprehensive selection of models, you can match your AI workloads to the perfect model every time.

Through Amazon Bedrock, you can seamlessly experiment with different models, mix and match capabilities, and switch between providers without rewriting code—turning model choice into a strategic advantage that helps you continuously evolve your AI strategy as new innovations emerge. At launch, these new models are available in Bedrock via an OpenAI compatible endpoint. You can point the OpenAI SDK to this endpoint or use the Bedrock InvokeModel and Converse API.

With SageMaker JumpStart, you can quickly evaluate, compare, and customize models for your use case. You can then deploy the original or the customized model in production with the SageMaker AI console or using the SageMaker Python SDK.

Let’s see how these work in practice.

Getting started with OpenAI open weight models in Amazon Bedrock
In the Amazon Bedrock console, I choose Model access from the Configure and learn section of the navigation pane. Then, I navigate to the two listed OpenAI models on this page and request access.

Now that I have access, I use the Chat/Test playground to test and evaluate the models. I select OpenAI as the category and then the gpt-oss-120b model.

Using this model, I run the following sample prompt:

A family has $5,000 to save for their vacation next year. They can place the money in a savings account earning 2% interest annually or in a certificate of deposit earning 4% interest annually but with no access to the funds until the vacation. If they need $1,000 for emergency expenses during the year, how should they divide their money between the two options to maximize their vacation fund?

This prompt generates an output that includes the chain of thought used to produce the result.

I can use these models with the OpenAI SDK by configuring the API endpoint (base URL) and using an Amazon Bedrock API key for authentication. For example, I set this environment variables to use the US West (Oregon) AWS Region endpoint (us-west-2) and my Amazon Bedrock API key:

export OPENAI_API_KEY="<my-bedrock-api-key>"
export OPENAI_BASE_URL="https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1"

Now I invoke the model using the OpenAI Python SDK.

client = OpenAI()

response = client.chat.completion.create(
    messages=[{
        "role": "user",
        "content": "Hello, how are you?"
    }],
    model="openai.gpt-oss-120b-1:0",
    stream=True
)

for item in response:
    print(item)

To build an AI agent, I can choose any framework that supports the Amazon Bedrock API or the OpenAI API. For example, here’s the starting code for Strands Agents using the Amazon Bedrock API:

from strands import Agent
from strands.models import BedrockModel
from strands_tools import calculator

model = BedrockModel(
    model_id="openai.gpt-oss-120b-1:0"
)
agent = Agent(
    model=model,
    tools=[calculator]
)

agent("Tell me the square root of 42 ^ 3")

I save the code (app.py file), install the dependencies, and run the agent locally:

pip install strands-agents strands-agents-tools
python app.py

When I am satisfied with the agent, I can deploy in production using the capabilities offered by Amazon Bedrock AgentCore, including a fully managed serverless runtime and memory and identity management.

Getting started with OpenAI open weight models in Amazon SageMaker JumpStart
In the Amazon SageMaker AI console, you can use OpenAI open weight models in the SageMaker Studio. The first time I do this, I need to set up a SageMaker domain. There are options to set it up for a single user (simpler) or an organization. For these tests, I use a single user setup.

In the SageMaker JumpStart model view, I have access to a detailed description of the gpt-oss-120b or gpt-oss-20b model.

I choose the gpt-oss-20b model and then deploy the model. In the next steps, I select the instance type and the initial instance count. After a few minutes, the deployment creates an endpoint that I can then invoke in SageMaker Studio and using any AWS SDKs.

To learn more, visit GPT OSS models from OpenAI are now available on SageMaker JumpStart in the AWS Artificial Intelligence Blog.

Things to know
The new OpenAI open weight models are now available in Amazon Bedrock in the US West (Oregon) AWS Region, while Amazon SageMaker JumpStart supports these models in US East (Ohio, N. Virginia) and Asia Pacific (Mumbai, Tokyo).

Each model comes equipped with full chain-of-thought output capabilities, providing you with detailed visibility into the model’s reasoning process. This transparency is particularly valuable for applications requiring high levels of interpretability and validation. These models give you the freedom to modify, adapt, and customize them to your specific needs. This flexibility allows you to fine-tune the models for your unique use cases, integrate them into your existing workflows, and even build upon them to create new, specialized models tailored to your industry or application.

Security and safety are built into the core of these models, with comprehensive evaluation processes and safety measures in place. The models maintain compatibility with the standard GPT-4 tokenizer.

Both models can be used in your preferred environment, whether that’s through the serverless experience of Amazon Bedrock or the extensive machine learning (ML) development capabilities of SageMaker JumpStart. For information about the costs associated with using these models and services, visit the Amazon Bedrock pricing and Amazon SageMaker AI pricing pages.

To learn more, see the parameters for the models and the chat completions API in the Amazon Bedrock documentation.

Get started today with OpenAI open weight models on AWS in the Amazon Bedrock console or in Amazon SageMaker AI console.

– Danilo

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Introducing Amazon Elastic VMware Service for running VMware Cloud Foundation on AWS

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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AWS Weekly Roundup: Amazon DocumentDB, AWS Lambda, Amazon EC2, and more (August 4, 2025)

This week brings an array of innovations spanning from generative AI capabilities to enhancements of foundational services. Whether you’re building AI-powered applications, managing databases, or optimizing your cloud infrastructure, these updates help build more advanced, robust, and flexible applications.

Last week’s launches
Here are the launches that got my attention this week:

• Amazon DocumentDB – Amazon DocumentDB Serverless is now available offering an on-demand, fully managed MongoDB API-compatible document database service. Read more in Channy’s post.
• Amazon Q Developer CLI – You can now create custom agents to help you customize the CLI agent to be more effective when performing specialized tasks such as code reviews and troubleshooting. More info in this blog.
• Amazon Bedrock Data Automation – Now supports DOC/DOCX files for document processing and H.265 encoded video files for video processing, making it easier to build multimodal data analysis pipelines.
• Amazon DynamoDB – Introduced the Amazon DynamoDB data modeling Model Context Protocol (MCP) tool, providing a structured, natural-language-driven workflow to translate application requirements into DynamoDB data models.
• AWS Lambda – Response streaming now supports a default maximum response payload size of 200 MB, 10 times higher than before. Lambda response streaming helps you build applications that progressively stream response payloads back to clients, improving performance for latency sensitive workloads by reducing time to first byte (TTFB) performance.
• Powertools for AWS – Introducing v2 of Powertools for AWS Lambda (Java), a developer toolkit that helps you implement serverless best practices and directly translates AWS Well-Architected recommendations.
• Amazon SNS – Now supports three additional message filtering operators: wildcard matching, anything-but wildcard matching, and anything-but prefix matching. SNS now also supports message group IDs in standard topics, enabling fair queue functionality for subscribed Amazon SQS standard queues.
• Amazon CloudFront – Now offers two capabilities to enhance origin timeout controls: a response completion timeout and support for custom response timeout values for Amazon S3 origins. These capabilities give you more control over how to handle slow or unresponsive origins.
• Amazon EC2 – You are now able to force terminate EC2 instances that are stuck in the shutting-down state.
• Amazon EC2 Auto Scaling – You can now use AWS Lambda functions as notification targets for EC2 Auto Scaling lifecycle hooks. For example, you can use this to trigger custom actions when an instance enters a wait state.
• Amazon SES – You can now provision isolated tenants within a single SES account and apply automated reputation policies to manage email sending.
• AWS Management Console – You can now view your AWS Applications in the Service menu in the console navigation bar. With this view you can see all your Applications and choose an Application to see all its associated resources.
• Amazon Connect – Amazon Connect UI builder now features an updated user interface to reduce the complexity to build structured workflows. It also simplified forecast editing with a new UI experience that improves planning accuracy. The Contact Control Panel now features an updated and more intuitive user interface. Amazon Connect also introduced new actions and workflows into the agent workspace. These actions are powered by third-party applications running in the background.
• AWS Clean Rooms – Now publishes events to Amazon EventBridge for status changes in a Clean Rooms collaboration, further simplifying how companies and their partners analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data.
• AWS Entity Resolution – Introduced rule-based fuzzy matching using Levenshtein Distance, Cosine Similarity, and Soundex algorithms to help resolve consumer records across fragmented, inconsistent, and often incomplete datasets.

Additional updates
Here are some additional projects, blog posts, and news items that I found interesting:

• Amazon Strands Agents SDK: A technical deep dive into agent architectures and observability – Nice overview to build single and multi-agent architectures.
• Build dynamic web research agents with the Strands Agents SDK and Tavily – Showing how easy it is to add a new tool.
• Structured outputs with Amazon Nova: A guide for builders – Good tips implemented on top of native tool use with constrained decoding.
• Automate the creation of handout notes using Amazon Bedrock Data Automation – A solution to build an automated, serverless solution to transform webinar recordings into comprehensive handouts.
• Build modern serverless solutions following best practices using Amazon Q Developer CLI and MCP – Adding the AWS Serverless MCP server.
• Introducing Amazon Bedrock AgentCore Browser Tool – More info on this tool that enables AI agents to interact seamlessly with websites.
• Introducing Amazon Bedrock AgentCore Code Interpreter – A fully managed service that enables AI agents to securely execute code in isolated sandbox environments.

Upcoming AWS events
Check your calendars so that you can sign up for these upcoming events:

AWS re:Invent 2025 (December 1-5, 2025, Las Vegas) — AWS’s flagship annual conference offering collaborative innovation through peer-to-peer learning, expert-led discussions, and invaluable networking opportunities.

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: Mexico City (August 6) and Jakarta (August 7).

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: Australia (August 15), Adria (September 5), Baltic (September 10), and Aotearoa (September 18).

Join the AWS Builder Center to learn, build, and connect with builders in the AWS community. Browse here upcoming in-person and virtual developer-focused events.

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

– Danilo

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