Today we’re announcing the general availability of second-generation AWS Outposts racks, which marks the latest innovation from AWS for edge computing. This new generation includes support for the latest x86-powered Amazon Elastic Compute Cloud (Amazon EC2) instances, new simplified network scaling and configuration, and accelerated networking instances designed specifically for ultra-low latency and high-throughput workloads. These enhancements deliver greater performance for a broad range of on-premises workloads, such as core trading systems of financial services and telecom 5G Core workloads.

Customers like athenahealth, FanDuel, First Abu Dhabi Bank, Mercado Libre, Liberty Latin America, Riot Games, Vector Limited, and Wiwynn are already using Outposts racks for workloads that need to stay on-premises. The second-generation Outposts rack can provide low latency, local data processing, or data residency needs, such as game servers for multi-player online games, customer transaction data, medical records, industrial and manufacturing control systems, telecom Business Support Systems (BSS), and edge inference of a variety of machine learning (ML) models. Customers can now take advantage of the latest generation of processors and more advanced configurations of Outposts racks to support faster processing, higher memory capacity, and increased network bandwidth.

Latest generation EC2 instances

We’re excited to announce local support for the latest generation (7th generation) of x86-powered Amazon EC2 instances on AWS Outposts racks, starting with C7i compute-optimized instances, M7i general-purpose instances, and R7i memory-optimized instances. These new instances deliver twice the vCPU, memory, and network bandwidth while providing up to 40% better performance compared to C5, M5, and R5 instances on previous generation Outposts racks. They are powered by 4th Gen Intel Xeon Scalable processors and are ideal for a broad range of on-premises workloads requiring enhanced performance such as larger databases, more memory-intensive applications, advanced real-time big data analytics, high-performance video encoding and streaming, and CPU-based edge inference with more sophisticated ML models. Support for more latest generation EC2 instances, including GPU-enabled instances, is coming soon.

Simplified network scaling and configuration

We’ve completely reimagined networking in our latest Outposts generation, making it simpler and more scalable than ever. At the heart of this upgrade is our new Outposts network rack, which acts as a central hub for all your compute and storage traffic.

This new design brings three major benefits to the table. First, you can now scale your compute resources independently from your networking infrastructure, giving you more flexibility and cost efficiency as your workloads grow. Second, we’ve built in network resilience from the ground up, with the network rack automatically handling device failures to keep your systems running smoothly. Third, connecting to your on-premises environment and AWS Regions is now a breeze – you can configure everything from IP addresses to VLAN and BGP settings through straightforward APIs or our updated console interface.

Specialized Amazon EC2 instances with accelerated networking

We’re introducing a new category of specialized Amazon EC2 instances on Outposts racks with accelerated networking. These instances are purpose built for the most latency-sensitive, compute-intensive, and throughput-intensive mission-critical workloads on-premises. To deliver the best possible performance, in addition to the Outpost logical network, these instances feature a secondary physical network with network accelerator cards connected to top-of-rack (TOR) switches.

First in this category are bmn-sf2e instances, designed for ultra-low latency with deterministic performance. The new instances run on Intel’s latest Sapphire Rapids processors (4th Gen Xeon Scalable), delivering 3.9 GHz sustained performance across all cores with generous memory allocation – 8GB of RAM for every CPU core. We’ve equipped bmn-sf2e instances with AMD Solarflare X2522 network cards that connect directly to top-of-rack switches.

For financial services customers, especially capital market firms, these instances offer deterministic networking through native Layer 2 (L2) multicast, precision time protocol (PTP), and equal cable lengths. This enables customers to meet regulatory requirements around fair trading and equal access while easily connecting to their existing trading infrastructure.

Instance Name	vCPUs	Memory (DDR5)	Network Bandwidth	NVMe SSD Storage	Accelerated Network Cards	Accelerated Bandwidth (Gbps)
bmn-sf2e.metal-16xl	64	512 GiB	25 Gbps	2 x 8 TB (16 TB)	2	100
bmn-sf2e.metal-32xl	128	1024 GiB	50 Gbps	4 x 8 TB (32 TB)	4	200

The second instance type, bmn-cx2, is optimized for high throughput and low latency. This instance features NVIDIA ConnectX-7 400G NICs physically connected to high-speed top-of-rack switches, delivering up to 800 Gbps bare metal network bandwidth operating at near line rate. With native Layer 2 (L2) multicast and hardware PTP support, this instance is ideal for high-throughput workloads like real-time market data distribution, risk analytics, and telecom 5G core network applications.

Instance Name	vCPUs	Memory (DDR5)	Network Bandwidth	NVMe SSD Storage	Accelerated Network Cards	Accelerated Bandwidth (Gbps)
bmn-cx2.metal-48xl	192	1536 GiB	50 Gbps	4 x 4 TB (16 TB)	2	800

Bottom line, the new generation of Outposts racks deliver enhanced performance, scalability, and resiliency for a broad range of on-premises workloads, even for mission-critical workloads with the most stringent latency and throughput requirements. You can make your selection and initiate your order from the AWS Management Console. The new instances maintain consistency with regional deployments by supporting the same APIs, AWS Management Console, automation, governance policies, and security controls in the cloud and on-premises, improving developer productivity and IT efficiency.

Things to know

At launch, second-generation Outposts racks can be shipped to US and Canada and be parented back to 6 AWS Regions including US East (N. Virginia and Ohio), US West (Oregon), EU West (London and France) and Asia Pacific (Singapore). Support for more countries and territories and AWS Regions is coming soon. At launch, second-generation Outposts racks locally support a subset of AWS services found in previous generation Outposts racks. Support for more EC2 instance types and more AWS services is coming soon.

To learn more, visit the AWS Outposts racks product page and user guide. You can also talk to an Outposts expert if you are ready to discuss your on-premises needs.

— Micah;

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The newest AI models form Meta, Llama 4 Scout 17B and Llama 4 Maverick 17B, are now available as a fully managed, serverless option in Amazon Bedrock. These new foundation models (FMs) deliver natively multimodal capabilities with early fusion technology that you can use for precise image grounding and extended context processing in your applications.

Llama 4 uses an innovative mixture-of-experts (MoE) architecture that provides enhanced performance across reasoning and image understanding tasks while optimizing for both cost and speed. This architectural approach enables Llama 4 to offer improved performance at lower cost compared to Llama 3, with expanded language support for global applications.

The models were already available on Amazon SageMaker JumpStart, and you can now use them in Amazon Bedrock to streamline building and scaling generative AI applications with enterprise-grade security and privacy.

Llama 4 Maverick 17B – A natively multimodal model featuring 128 experts and 400 billion total parameters. It excels in image and text understanding, making it suitable for versatile assistant and chat applications. The model supports a 1 million token context window, giving you the flexibility to process lengthy documents and complex inputs.

Llama 4 Scout 17B – A general-purpose multimodal model with 16 experts, 17 billion active parameters, and 109 billion total parameters that delivers superior performance compared to all previous Llama models. Amazon Bedrock currently supports a 3.5 million token context window for Llama 4 Scout, with plans to expand in the near future.

Use cases for Llama 4 models
You can use the advanced capabilities of Llama 4 models for a wide range of use cases across industries:

Enterprise applications – Build intelligent agents that can reason across tools and workflows, process multimodal inputs, and deliver high-quality responses for business applications.

Multilingual assistants – Create chat applications that understand images and provide high-quality responses across multiple languages, making them accessible to global audiences.

Code and document intelligence – Develop applications that can understand code, extract structured data from documents, and provide insightful analysis across large volumes of text and code.

Customer support – Enhance support systems with image analysis capabilities, enabling more effective problem resolution when customers share screenshots or photos.

Content creation – Generate creative content across multiple languages, with the ability to understand and respond to visual inputs.

Research – Build research applications that can integrate and analyze multimodal data, providing insights across text and images.

Using Llama 4 models in Amazon Bedrock
To use these new serverless models in Amazon Bedrock, I first need to request access. In the Amazon Bedrock console, I choose Model access from the navigation pane to toggle access to Llama 4 Maverick 17B and Llama 4 Scout 17B models.

The Llama 4 models can be easily integrated into your applications using the Amazon Bedrock Converse API, which provides a unified interface for conversational AI interactions.

Here’s an example of how to use the AWS SDK for Python (Boto3) with Llama 4 Maverick for a multimodal conversation:

import boto3
import json
import os

AWS_REGION = "us-west-2"
MODEL_ID = "us.meta.llama4-maverick-17b-instruct-v1:0"
IMAGE_PATH = "image.jpg"


def get_file_extension(filename: str) -> str:
    """Get the file extension."""
    extension = os.path.splitext(filename)[1].lower()[1:] or 'txt'
    if extension == 'jpg':
        extension = 'jpeg'
    return extension


def read_file(file_path: str) -> bytes:
    """Read a file in binary mode."""
    try:
        with open(file_path, 'rb') as file:
            return file.read()
    except Exception as e:
        raise Exception(f"Error reading file {file_path}: {str(e)}")

bedrock_runtime = boto3.client(
    service_name="bedrock-runtime",
    region_name=AWS_REGION
)

request_body = {
    "messages": [
        {
            "role": "user",
            "content": [
                {
                    "text": "What can you tell me about this image?"
                },
                {
                    "image": {
                        "format": get_file_extension(IMAGE_PATH),
                        "source": {"bytes": read_file(IMAGE_PATH)},
                    }
                },
            ],
        }
    ]
}

response = bedrock_runtime.converse(
    modelId=MODEL_ID,
    messages=request_body["messages"]
)

print(response["output"]["message"]["content"][-1]["text"])

This example demonstrates how to send both text and image inputs to the model and receive a conversational response. The Converse API abstracts away the complexity of working with different model input formats, providing a consistent interface across models in Amazon Bedrock.

For more interactive use cases, you can also use the streaming capabilities of the Converse API:

response_stream = bedrock_runtime.converse_stream(
    modelId=MODEL_ID,
    messages=request_body['messages']
)

stream = response_stream.get('stream')
if stream:
    for event in stream:

        if 'messageStart' in event:
            print(f"\nRole: {event['messageStart']['role']}")

        if 'contentBlockDelta' in event:
            print(event['contentBlockDelta']['delta']['text'], end="")

        if 'messageStop' in event:
            print(f"\nStop reason: {event['messageStop']['stopReason']}")

        if 'metadata' in event:
            metadata = event['metadata']
            if 'usage' in metadata:
                print(f"Usage: {json.dumps(metadata['usage'], indent=4)}")
            if 'metrics' in metadata:
                print(f"Metrics: {json.dumps(metadata['metrics'], indent=4)}")

With streaming, your applications can provide a more responsive experience by displaying model outputs as they are generated.

Things to know
The Llama 4 models are available today with a fully managed, serverless experience in Amazon Bedrock in the US East (N. Virginia) and US West (Oregon) AWS Regions. You can also access Llama 4 in US East (Ohio) via cross-region inference.

As usual with Amazon Bedrock, you pay for what you use. For more information, see Amazon Bedrock pricing.

These models support 12 languages for text (English, French, German, Hindi, Italian, Portuguese, Spanish, Thai, Arabic, Indonesian, Tagalog, and Vietnamese) and English when processing images.

To start using these new models today, visit the Meta Llama models section in the Amazon Bedrock User Guide. You can also explore how our Builder communities are using Amazon Bedrock in their solutions in the generative AI section of our community.aws site.

— Danilo

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Today, I’m happy to announce the general availability of Amazon CloudFront SaaS Manager, a new feature that helps software-as-a-service (SaaS) providers, web development platform providers, and companies with multiple brands and websites efficiently manage delivery across multiple domains. Customers already use CloudFront to securely deliver content with low latency and high transfer speeds. CloudFront SaaS Manager addresses a critical challenge these organizations face: managing tenant websites at scale, each requiring TLS certificates, distributed denial-of-service (DDoS) protection, and performance monitoring.

With CloudFront Saas Manager, web development platform providers and enterprise SaaS providers who manage a large number of domains will use simple APIs and reusable configurations that use CloudFront edge locations worldwide, AWS WAF, and AWS Certificate Manager. CloudFront SaaS Manager can dramatically reduce operational complexity while providing high-performance content delivery and enterprise-grade security for every customer domain.

How it works
In CloudFront, you can use multi-tenant SaaS deployments, a strategy where a single CloudFront distribution serves content for multiple distinct tenants (users or organizations). CloudFront SaaS Manager uses a new template-based distribution model called a multi-tenant distribution to serve content across multiple domains while sharing configuration and infrastructure. However, if supporting single websites or application, a standard distribution would be better or recommended.

A template distribution defines the base configuration that will be used across domains such as origin configurations, cache behaviors, and security settings. Each template distribution has a distribution tenant to represent domain-specific origin paths or origin domain names including web access control list (ACL) overrides and custom TLS certificates.

Optionally, multiple distribution tenants can use the same connection group that provides the CloudFront routing endpoint that serves content to viewers. DNS records point to the CloudFront endpoint of the connection group using a Canonical Name Record (CNAME).

To learn more, visit Understand how multi-tenant distributions work in the Amazon CloudFront Developer Guide.

CloudFront SaaS Manager in action
I’d like to give you an example to help you understand the capabilities of CloudFront SaaS Manager. You have a company called MyStore, a popular e-commerce platform that helps your customer easily set up and manage an online store. MyStore’s tenants already enjoy outstanding customer service, security, reliability, and ease-of-use with little setup required to get a store up and running, resulting in 99.95 percent uptime for the last 12 months.

Customers of MyStore are unevenly distributed across three different pricing tiers: Bronze, Silver, and Gold, and each customer is assigned a persistent mystore.app subdomain. You can apply these tiers to different customer segments, customized settings, and operational Regions. For example, you can add AWS WAF service in the Gold tier as an advanced feature. In this example, MyStore has decided not to maintain their own web servers to handle TLS connections and security for a growing number of applications hosted on their platform. They are evaluating CloudFront to see if that will help them reduce operational overhead.

Let’s find how as MyStore you configure your customer’s websites distributed in multiple tiers with the CloudFront SaaS Manager. To get started, you can create a multi-tenant distribution that acts as a template corresponding to each of the three pricing tiers the MyStore offers: Bronze, Sliver, and Gold shown in Multi-tenant distribution under the SaaS menu on the Amazon CloudFront console.

To create a multi-tenant distribution, choose Create distribution and select Multi-tenant architecture if you have multiple websites or applications that will share the same configuration. Follow the steps to provide basic details such as a name for your distribution, tags, and wildcard certificate, specify origin type and location for your content such as a website or app, and enable security protections with AWS WAF web ACL feature.

When the multi-tenant distribution is created successfully, you can create a distribution tenant by choosing Create tenant in the Distribution tenants menu in the left navigation pane. You can create a distribution tenant to add your active customer to be associated with the Bronze tier.

Each tenant can be associated with up to one multi-tenant distribution. You can add one or more domains of your customers to a distribution tenant and assign custom parameter values such as origin domains and origin paths. A distribution tenant can inherit the TLS certificate and security configuration of its associated multi-tenant distribution. You can also attach a new certificate specifically for the tenant, or you can override the tenant security configuration.

When the distribution tenant is created successfully, you can finalize this step by updating a DNS record to route traffic to the domain in this distribution tenant and creating a CNAME pointed to the CloudFront application endpoint. To learn more, visit Create a distribution in the Amazon CloudFront Developer Guide.

Now you can see all customers in each distribution tenant to associate multi-tenant distributions.

By increasing customers’ business needs, you can upgrade your customers from Bronze to Silver tiers by moving those distribution tenants to a proper multi-tenant distribution.

During the monthly maintenance process, we identify domains associated with inactive customer accounts that can be safely decommissioned. If you’ve decided to deprecate the Bronze tier and migrate all customers who are currently in the Bronze tier to the Silver tier, then you can delete a multi-tenant distribution to associate the Bronze tier. To learn more, visit Update a distribution or Distribution tenant customizations in the Amazon CloudFront Developer Guide.

By default, your AWS account has one connection group that handles all your CloudFront traffic. You can enable Connection group in the Settings menu in the left navigation pane to create additional connection groups, giving you more control over traffic management and tenant isolation.

To learn more, visit Create custom connection group in the Amazon CloudFront Developer Guide.

Now available
Amazon CloudFront SaaS Manager is available today. To learn about, visit CloudFront SaaS Manager product page and documentation page. To learn about SaaS on AWS, visit AWS SaaS Factory.

Give CloudFront SaaS Manager a try in the CloudFront console today and send feedback to AWS re:Post for Amazon CloudFront or through your usual AWS Support contacts.

— Veliswa.
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One thing we’ve witnessed in recent months is the expansion of context windows in foundation models (FMs), with many now handling sequence lengths that would have been unimaginable just a year ago. However, building AI-powered applications that can process vast amounts of information while maintaining the reliability and security standards required for enterprise use remains challenging.

For these reasons, we’re excited to announce that Writer Palmyra X5 and X4 models are available today in Amazon Bedrock as a fully managed, serverless offering. AWS is the first major cloud provider to deliver fully managed models from Writer. Palmyra X5 is a new model launched today by Writer. Palmyra X4 was previously available in Amazon Bedrock Marketplace.

Writer Palmyra models offer robust reasoning capabilities that support complex agent-based workflows while maintaining enterprise security standards and reliability. Palmyra X5 features a one million token context window, and Palmyra X4 supports a 128K token context window. With these extensive context windows, these models remove some of the traditional constraints for app and agent development, enabling deeper analysis and more comprehensive task completion.

With this launch, Amazon Bedrock continues to bring access to the most advanced models and the tools you need to build generative AI applications with security, privacy, and responsible AI.

As a pioneer in FM development, Writer trains and fine-tunes its industry leading models on Amazon SageMaker HyperPod. With its optimized distributed training environment, Writer reduces training time and brings its models to market faster.

Palmyra X5 and X4 use cases
Writer Palmyra X5 and X4 are designed specifically for enterprise use cases, combining powerful capabilities with stringent security measures, including System and Organization Controls (SOC) 2, Payment Card Industry Data Security Standard (PCI DSS), and Health Insurance Portability and Accountability Act (HIPAA) compliance certifications.

Palmyra X5 and X4 models excel in various enterprise use cases across multiple industries:

Financial services – Palmyra models power solutions across investment banking and asset and wealth management, including deal transaction support, 10-Q, 10-K and earnings transcript highlights, fund and market research, and personalized client outreach at scale.

Healthcare and life science – Payors and providers use Palmyra models to build solutions for member acquisition and onboarding, appeals and grievances, case and utilization management, and employer request for proposal (RFP) response. Pharmaceutical companies use these models for commercial applications, medical affairs, R&D, and clinical trials.

Retail and consumer goods – Palmyra models enable AI solutions for product description creation and variation, performance analysis, SEO updates, brand and compliance reviews, automated campaign workflows, and RFP analysis and response.

Technology – Companies across the technology sector implement Palmyra models for personalized and account-based marketing, content creation, campaign workflow automation, account preparation and research, knowledge support, job briefs and candidate reports, and RFP responses.

Palmyra models support a comprehensive suite of enterprise-grade capabilities, including:

Adaptive thinking – Hybrid models combining advanced reasoning with enterprise-grade reliability, excelling at complex problem-solving and sophisticated decision-making processes.

Multistep tool-calling – Support for advanced tool-calling capabilities that can be used in complex multistep workflows and agentic actions, including interaction with enterprise systems to perform tasks like updating systems, executing transactions, sending emails, and triggering workflows.

Enterprise-grade reliability – Consistent, accurate results while maintaining strict quality standards required for enterprise use, with models specifically trained on business content to align outputs with professional standards.

Using Palmyra X5 and X4 in Amazon Bedrock
As for all new serverless models in Amazon Bedrock, I need to request access first. In the Amazon Bedrock console, I choose Model access from the navigation pane to enable access to Palmyra X5 and Palmyra X4 models.

When I have access to the models, I can start building applications with any AWS SDKs using the Amazon Bedrock Converse API. The models use cross-Region inference with these inference profiles:

• For Palmyra X5: us.writer.palmyra-x5-v1:0
• For Palmyra X4: us.writer.palmyra-x4-v1:0

Here’s a sample implementation with the AWS SDK for Python (Boto3). In this scenario, there is a new version of an existing product. I need to prepare a detailed comparison of what’s new. I have the old and new product manuals. I use the large input context of Palmyra X5 to read and compare the two versions of the manual and prepare a first draft of the comparison document.

import sys
import os
import boto3
import re

AWS_REGION = "us-west-2"
MODEL_ID = "us.writer.palmyra-x5-v1:0"
DEFAULT_OUTPUT_FILE = "product_comparison.md"

def create_bedrock_runtime_client(region: str = AWS_REGION):
    """Create and return a Bedrock client."""
    return boto3.client('bedrock-runtime', region_name=region)

def get_file_extension(filename: str) -> str:
    """Get the file extension."""
    return os.path.splitext(filename)[1].lower()[1:] or 'txt'

def sanitize_document_name(filename: str) -> str:
    """Sanitize document name."""
    # Remove extension and get base name
    name = os.path.splitext(filename)[0]
    
    # Replace invalid characters with space
    name = re.sub(r'[^a-zA-Z0-9\s\-\(\)\[\]]', ' ', name)
    
    # Replace multiple spaces with single space
    name = re.sub(r'\s+', ' ', name)
    
    # Strip leading/trailing spaces
    return name.strip()

def read_file(file_path: str) -> bytes:
    """Read a file in binary mode."""
    try:
        with open(file_path, 'rb') as file:
            return file.read()
    except Exception as e:
        raise Exception(f"Error reading file {file_path}: {str(e)}")

def generate_comparison(client, document1: bytes, document2: bytes, filename1: str, filename2: str) -> str:
    """Generate a markdown comparison of two product manuals."""
    print(f"Generating comparison for {filename1} and {filename2}")
    try:
        response = client.converse(
            modelId=MODEL_ID,
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "text": "Please compare these two product manuals and create a detailed comparison in markdown format. Focus on comparing key features, specifications, and highlight the main differences between the products."
                        },
                        {
                            "document": {
                                "format": get_file_extension(filename1),
                                "name": sanitize_document_name(filename1),
                                "source": {
                                    "bytes": document1
                                }
                            }
                        },
                        {
                            "document": {
                                "format": get_file_extension(filename2),
                                "name": sanitize_document_name(filename2),
                                "source": {
                                    "bytes": document2
                                }
                            }
                        }
                    ]
                }
            ]
        )
        return response['output']['message']['content'][0]['text']
    except Exception as e:
        raise Exception(f"Error generating comparison: {str(e)}")

def main():
    if len(sys.argv) < 3 or len(sys.argv) > 4:
        cmd = sys.argv[0]
        print(f"Usage: {cmd} <manual1_path> <manual2_path> [output_file]")
        sys.exit(1)

    manual1_path = sys.argv[1]
    manual2_path = sys.argv[2]
    output_file = sys.argv[3] if len(sys.argv) == 4 else DEFAULT_OUTPUT_FILE
    paths = [manual1_path, manual2_path]

    # Check each file's existence
    for path in paths:
        if not os.path.exists(path):
            print(f"Error: File does not exist: {path}")
            sys.exit(1)

    try:
        # Create Bedrock client
        bedrock_runtime = create_bedrock_runtime_client()

        # Read both manuals
        print("Reading documents...")
        manual1_content = read_file(manual1_path)
        manual2_content = read_file(manual2_path)

        # Generate comparison directly from the documents
        print("Generating comparison...")
        comparison = generate_comparison(
            bedrock_runtime,
            manual1_content,
            manual2_content,
            os.path.basename(manual1_path),
            os.path.basename(manual2_path)
        )

        # Save comparison to file
        with open(output_file, 'w') as f:
            f.write(comparison)

        print(f"Comparison generated successfully! Saved to {output_file}")

    except Exception as e:
        print(f"Error: {str(e)}")
        sys.exit(1)

if __name__ == "__main__":
    main()

To learn how to use Amazon Bedrock with AWS SDKs, browse the code samples in the Amazon Bedrock User Guide.

Things to know
Writer Palmyra X5 and X4 models are available in Amazon Bedrock today in the US West (Oregon) AWS Region with cross-Region inference. For the most up-to-date information on model support by Region, refer to the Amazon Bedrock documentation. For information on pricing, visit Amazon Bedrock pricing.

These models support English, Spanish, French, German, Chinese, and multiple other languages, making them suitable for global enterprise applications.

Using the expansive context capabilities of these models, developers can build more sophisticated applications and agents that can process extensive documents, perform complex multistep reasoning, and handle sophisticated agentic workflows.

To start using Writer Palmyra X5 and X4 models today, visit the Writer model section in the Amazon Bedrock User Guide. You can also explore how our Builder communities are using Amazon Bedrock in their solutions in the generative AI section of our community.aws site.

Let us know what you build with these powerful new capabilities!

— Danilo

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Summit season is in full throttle! If you haven’t been to an AWS Summit, I highly recommend you check one out that’s nearby. They are large-scale all-day events where you can attend talks, watch interesting demos and activities, connect with AWS and industry people, and more. Best of all, they are free—so all you need to do is register! You can find a list of them here in the AWS Events page. Incidentally, you can also discover other AWS events going in your area on that same page; just use the filters on the side to find something that interests you.

Speaking of AWS Summits, this week is the AWS Summit London (April 30). It’s local for me, and I have been heavily involved in the planning. You do not want to miss this! Make sure to check it out and hopefully I’ll be seeing you there.

Ready to find out some highlights from last week’s exciting AWS launches? Let’s go!

New features and capabilities highlights
Let’s start by looking at some of the enhancements launched last week.

• Amazon Q Developer releases state of the art agent for feature development — AWS has announced an update to Amazon Q Developer’s software development agent, which achieves state-of-the-art performance on industry benchmarks and can generate multiple candidate solutions for coding problems. This new agent provides more reliable suggestions helping to reduce debugging time and enabling developers to focus on higher-level design and innovation.
• Amazon Cognito now supports refresh token rotation — Amazon Cognito now supports OAuth 2.0 refresh token rotation, allowing user pool clients to automatically replace existing refresh tokens with new ones at regular intervals, enhancing security without requiring users to re-authenticate. This feature helps customers achieve both seamless user experience and improved security by automatically updating refresh tokens frequently, rather than having to choose between long-lived tokens for convenience, or short-lived tokens for security.
• Amazon Bedrock Intelligent Prompt Routing is now generally available — Amazon Bedrock’s Intelligent Prompt Routing, now generally available, automatically routes prompts to different foundation models within a model family to optimize response quality and cost. The service now offers increased configurability across multiple model families including Claude (Anthropic), Llama (Meta), and Nova (Amazon), allowing users to choose any two models from a family and set custom routing criteria.
• Upgrades to Amazon Q Business integrations for M365 Word and Outlook — Amazon Q Business integrations for Microsoft Word and Outlook now have the ability to search company knowledge bases, support image attachments, and handle larger context windows for more detailed prompts. These enhancements enable users to seamlessly access indexed company data and incorporate richer content while working on documents and emails, without needing to switch between different applications or contexts.

Security
There were a few new security improvements released last week, but these are the ones that caught my eye:

• AWS Account Management now supports account name update via authorized IAM principals — AWS now allows IAM principals to update account names, removing the previous requirement for root user access. This applies to both standalone accounts and member accounts within AWS Organizations, where authorized IAM principals in management and delegated admin accounts can manage account names centrally.
• AWS Resource Explorer now supports AWS PrivateLink — AWS Resource Explorer now supports AWS PrivateLink across all commercial Regions, enabling secure resource discovery and search capabilities across AWS Regions and accounts within your VPC, without requiring public internet access.
• Amazon SageMaker Lakehouse now supports attribute based access control — Amazon SageMaker Lakehouse now supports attribute-based access control (ABAC), allowing administrators to manage data access permissions using dynamic attributes associated with IAM identities rather than creating individual policies. This simplifies access management by enabling permissions to be automatically granted to any IAM principal with matching tags, making it more efficient to handle access control as teams grow.

Networking
As you may be aware, there is a growing industry push to adopt IPv6 as the default protocol for new systems while migrating existing infrastructure where possible. This week, two more services have added their support to help customers towards that goal:

• Amazon SQS now supports Internet Protocol Version 6 (IPv6) — Amazon SQS now supports IPv6 for API requests, allowing customers to communicate using IPv6, IPv4, or dual stack clients through public endpoints.
• AWS AppConfig now supports Internet Protocol Version 6 (IPv6) — Now supports both IPv4 and IPv6 endpoints while preserving existing IPv4 functionality.

Capacity and costs
Customers using Amazon Kinesis Data Streams can enjoy higher default quotas, while Amazon Redshift Serverless customers get a new cost saving opportunity.

• Amazon Kinesis Data Streams increases default shard limits to up to 20,000 per AWS account — Amazon Kinesis Data Streams now supports higher default processing capacity with up to 20,000 shards per account in certain Regions, enabling customers to handle 10 GB/sec input and 20 GB/sec output without requesting limit increases.
• Serverless Reservations for Amazon Redshift Serverless — You can now reduce Amazon Redshift Serverless costs by up to 24% by committing to a specific RPU capacity for one year, choosing either to pay nothing upfront for a 20% discount or pay all upfront for maximum savings.

For a full list of AWS announcements, be sure to visit the What’s New with AWS? page.

Recommended Learning Resources
Everyone’s talking about MCP recently! Here are two great blog posts that I think will help you catch up and learn more about the possibilities of how to use MCP on AWS.

• Model Context Protocol (MCP) and Amazon Bedrock — In this post, Giuseppe Battista shows you how to use MCP with Amazon Bedrock.
• Scaling MCP Across Your Organization Through AWS Lambda — A very cool take by Chris Williams on how you can keep your MCP server locally while registering remote tools running on AWS Lambda for scalability and security.

Our Weekly Roundup is published every Monday to help you keep up with AWS launches, so don’t forget to check it again next week for more exciting news!

Enjoy the rest of your day!

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