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Twenty years ago today, Jeff Barr wrote a blog post that launched the Amazon EC2 Beta. That single post introduced resizable Linux virtual servers in the cloud, billed by the hour, with one instance type (m1.small) in one Region (US East). It was minimal yet useful, and it changed how the world thinks about computing infrastructure.

In 2021, Jeff covered the fifteen years of EC2 with the backstory and memorable EC2 launches. Over the last five years, AWS has continued to push the boundaries of what cloud computing can deliver, building custom silicon for general-purpose and AI workloads and expanding EC2 into new form factors and deployment models that our customers in 2006 could not have imagined.

The 20 years in brief
In his 15th anniversary post, Jeff chose important milestones of EC2 that established the foundational building blocks that customers still rely on today. Amazon Elastic Block Store (2008) provided persistent block storage. Elastic Load Balancing, Auto Scaling, and Amazon CloudWatch (2009) made applications scalable and highly available. Amazon Virtual Private Cloud (2009) gave customers logically isolated networks. AWS Nitro System (2017) enabled faster innovation and enhanced security. AWS Graviton processors (2018) were designed for cost-sensitive scale-out workloads.

Over 20 years, EC2 grew from one to over 1,200 instance types to meet customer needs across general-purpose, compute-, memory-, and storage-optimized, accelerated computing, and high-performance computing families. These instances expanded from one AWS Region to 39 Regions globally. AWS also extended EC2 beyond the Region boundary with AWS Outposts (2018) running EC2 instances locally, AWS Local Zones (2019) place globally, and AWS Wavelength (2019) inside global 5G telecommunications carrier networks.

While I hate to play favorites, I want to choose some of my favorite EC2 launches of the past five years:

• AWS Inferentia for ML inference at scale (2019): We introduced purpose-built ML inference instances (inf1) with AWS Inferentia chips. Amazon EC2 Inf2 instances became generally available in April 2023 for large-scale generative AI inference workloads. Together with the Inferentia family, AWS Trainium instances now give customers a full stack of AWS-designed silicon optimized for every phase of the AI lifecycle both inference and training.
• EC2 Mac instances (2020): The first Mac instances (mac1) were built on Apple Mac mini with Intel Core i7 (Coffee Lake) on the AWS Nitro System. Mac M1 (mac2) instances launched in July 2022 as the first Arm-based macOS instances on EC2. M2 Pro Mac instances followed in 2023, M4 and M4 Pro Mac instances in 2025, M3 Ultra Mac instances and M4 Max Mac instances in 2026, giving Apple developers a complete range of cloud-based build and test environments for macOS, iOS, iPadOS, tvOS, watchOS, and visionOS apps.
• AWS Trainium for full-stack AI workloads at scale (2021): In November 2021, we previewed Trn1 instances with AWS Trainium accelerators optimized for high-performance deep learning training. In December 2024, Trn2 instances powered by AWS Trainium2 launched, with Trn2 UltraServers linking 64 Trainium2 accelerators via NeuronLink for training trillion-parameter foundation models. At AWS re:Invent 2025, Trn3 UltraServers powered by AWS Trainium3 deliver the best token economics for next-generation agentic, reasoning, and video generation applications. A single Trn3 UltraServer interconnects up to 144 Trainium3 chips to train and serve the largest frontier models. Now, AWS Trainium3 delivers the leading price-performance for high-performance AI training and inference at scale.
• EC2 Capacity Blocks for ML (2023): This new EC2 usage model further democratizes ML democratizes ML by making it easy to access GPU instances to train and deploy ML and generative AI models. You reserve the GPU capacity you need (initially P5 instances) for a future date and only for the duration you require. In November 2024, EC2 Capacity Blocks for ML added supported for provisioning in a matter of minutes and extending up to six months. Now, EC2 Capacity Blocks for ML supports P6-B300, P6-B200, P5e, P5en, P4d, P4de, Trn1, Trn2, and Trn3 instances in addition to P5.
• AWS Graviton5 (2025): Building on eight years of Graviton innovation since 2018, we previewed Graviton5 chips in AWS re:Invent 2025 and launched M9g and M9gd instances powered by Graviton5 and built on the sixth-generation AWS Nitro System. C9g and C9gd followed in June 2026. Now, Graviton5 features 192 cores, a 5x larger cache, and up to 33% lower inter-core latency, making it well suited for the growing demands of agentic AI workloads such as real-time reasoning, code generation, and multi-step task orchestration that require continuous, high-throughput CPU compute at scale.
• AWS Nitro Isolation Engine (2026): Customers wanted to see, not just hear from us, proof of workload isolation in the Nitro Hypervisor. The Nitro Isolation Engine is a purpose-built component inside the Nitro Hypervisor, harnessing formal verification to provide mathematical assurance that customer workloads are isolated from each other and AWS operators, pioneering a new standard for mathematically proven cloud security. This feature is also based on the sixth-generation AWS Nitro System which has continued to evolve since our introduction in 2017.

The foundation underneath it all
Despite two decades of innovation, the fundamental value proposition of Amazon EC2 has not changed. Customers use it to get secure, resizable compute capacity in minutes, pay only for what they consume, and scale on demand without long-term commitments. That same flexibility now extends to AI workloads at a scale no one anticipated in 2006.

EC2 remains the foundational compute layer of AWS. Amazon ECS, Amazon EKS, AWS Lambda, AWS Fargate, AWS Batch, Amazon EMR, Amazon SageMaker AI, and Amazon Bedrock ultimately run on EC2 capacity. Every architectural pattern customers have built over the past twenty years, from simple web servers to trillion-parameter foundation model training clusters, starts with a decision to launch an instance.

We made strong foundational decisions in 2006, and we left room for the service to grow. Twenty years later, that strategy of creating services that are minimal-yet-useful, launching quickly, and iterating rapidly in response to your feedback continues to guide how we build. The next twenty years of cloud computing will demand capabilities we have not yet imagined. Amazon EC2 will continue to be the foundation where your workloads run.

To learn more about Amazon EC2, visit the Amazon EC2 product page or check out what’s new with EC2.

— Channy

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Today, we are announcing the general availability of AWS Glue 6.0, delivering 30% lower pricing than previous AWS Glue versions and introducing full support for Apache Iceberg v3 features. AWS Glue 6.0 is built on a fully modernized runtime, Apache Spark 4.1, Python 3.12, and Scala 2.13, delivering faster performance.

With this release, AWS Glue provides the most complete Iceberg v3 implementation on any fully serverless managed Spark service, along with new capabilities that simplify ETL authoring, improve PySpark performance, and enable real-time streaming with single-digit millisecond latency.

What is new in AWS Glue 6.0
AWS Glue 6.0 delivers the complete Apache Iceberg v3 specification, built on Iceberg 1.11.0. The headline feature is the VARIANT data type with shredding support, which achieves faster query read performance compared to traditional string data type columns for semi-structured data.

With VARIANT shredding, you can store and query JSON, logs, and event data without flattening schemas, eliminating duplicate data copies, custom parsing code, and pipeline breakage when schemas change. This capability transforms how teams handle semi-structured data at scale.

Additional Iceberg v3 capabilities include:

• Geometry and Geography data types: Enable native spatial processing for GIS analytics, location intelligence, and geospatial data pipelines directly on managed Spark.
• Nanosecond-precision timestamps: Support IoT sensor data, scientific computing, and high-frequency financial workloads that require precision beyond standard milliseconds.
• Unknown type handling: Process data with unexpected or evolving schemas without pipeline failures, providing resilience against upstream schema changes.

AWS Glue 6.0 also includes most significant upgrade in Spark 4.1, the modern runtime engine:

• Spark declarative pipelines: Spark Declarative Pipelines introduces a simplified approach to ETL authoring. Data engineers declare transformations, specifying what data should look like, while the engine automatically determines execution order and optimization. This reduces the complexity of pipeline development and eliminates manual orchestration overhead.
• Arrow-native Python UDFs and UDTFs: AWS Glue 6.0 introduces Arrow-native execution for Python User-Defined Functions (UDFs) and User-Defined Table Functions (UDTFs). This eliminates serialization overhead between Python and the JVM, improving PySpark performance for complex transformations.
• Real-time streaming mode: For stateless streaming use cases, AWS Glue 6.0 introduces a real-time streaming mode that achieves single-digit millisecond latency. Built on Spark 4.1’s Real-Time Mode with Glue-optimized execution, this capability supports real-time event processing, low-latency data transformation pipelines, and time-sensitive data routing.

Getting started with AWS Glue 6.0
No API changes are required to use AWS Glue 6.0. You can select the new version using the existing --glue-version parameter in the create-job or update-job APIs through AWS Command Line Interface (AWS CLI), AWS SDK, AWS Glue Studio, Amazon SageMaker Unified Studio, and your preferred IDE.

To get started with AWS Glue 6.0 jobs in the AWS Glue Studio console, open the AWS Glue job and on the Job Details tab, choose the version Glue 6.0 – Supports Spark 4.1, Scala 2, Python 3. You can create new AWS Glue jobs on AWS Glue 6.0 to get the benefit from the improvements, or migrate your existing AWS Glue jobs.

To start using AWS Glue 6.0 on an AWS Glue Studio notebook or an interactive session through a Jupyter notebook, set 6.0 in the %glue_version magic. You can also upgrade existing jobs to Glue 6.0 using the Spark upgrade agent on AWS Glue Studio or use the auto-upgrade feature in their existing Glue jobs to automatically upgrade them to Glue 6.0.

To learn more, visit the AWS Glue 6.0 version detail and Migrating AWS Glue for Spark jobs to AWS Glue version 6.0 in the AWS documentation.

Now available
AWS Glue 6.0 is generally available today in all AWS Regions where AWS Glue operates. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. If you want to call APIs, search documentation, find regional availability, and check troubleshooting about this new feature, try using the AWS MCP Server and plugins with your preferred AI tool.

You pay an hourly rate, billed by the second, for crawlers (discovering data) and extract, transform, and load (ETL) jobs (processing and loading data). For the AWS Glue Data Catalog, you pay a simplified monthly fee for storing and accessing the metadata. The first million objects stored are free, and the first million accesses are free. To learn more, visit AWS Glue Pricing page.

Give it a try in the AWS Glue Studio console, and send feedback to AWS re:Post for AWS Glue or through your usual AWS support contacts.

— Channy

This post was originally published on this site

Last week, the OpenSearch and Valkey teams visited Seoul to meet open source developers and contributors in the Open Source Summit Korea 2026 and MCP DevSummit Seoul 2026. At the four-day event, community leaders and users of open source projects and emerging agent AI gathered to share knowledge, collaborate on solutions, and push the projects forward.

Leaders of the Korean OpenSearch communities volunteered to participate in the booth, and also had time to network and interact in the user group meetup.

OpenSearch is an open source, enterprise-grade search and observability suite that brings order to unstructured data at scale. On June 9, 2026, OpenSearch 3.7 introduced new tools designed to query, alert, and track SLOs across logs, traces, and metrics through a single interface and retrieve vectors up to 5.5x faster for improved search performance. Since July 30, 2026, you can run OpenSearch version 3.7 on Amazon OpenSearch Service for improvements in vector search performance, search relevance, and Query Insights.

Valkey is an open source high-performance key/value datastore that supports a variety of workloads such as caching, message queues, and it can act as a primary database. On May 19, 2026, Valkey 9.1 introduced a redesigned I/O threading model that improves throughput by up to 17% and reduces memory usage for strings under 128 bytes by up to 20%. Since June 23, 2026, you can run Valkey 9.1 in Amazon ElastiCache for node-based clusters, delivering higher throughput, improved memory efficiency, and stronger access control for multi-tenant workloads.

You can meet our open source teams at upcoming OpenSearch and Valkey events.

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

• Amazon EC2 application status checks: Amazon EC2 introduces a new status check that helps you detect and respond to application-level issues on your EC2 instances. With application status checks, EC2 monitors applications to detect issues such as a web server that has stopped accepting requests, a Docker daemon that is not running, an incorrect networking configuration, or a network interface that is no longer passing traffic. To learn more, visit the Application status checks documentation.
• AWS IAM role manager to set up IAM roles automatically: You can use a new role manager that automatically sets up the IAM roles your AWS services need. When you set up a supported service in the console, role manager creates a default role on your behalf, or reuses one that already exists in your account if it matches the required permissions. Role manager supports six AWS service consoles at launch. To learn more, read How AWS IAM role manager rethinks the starting point for IAM roles.
• OpenAI Daybreak available to eligible customers on Amazon Bedrock: Daybreak is the cyber defense initiative from OpenAI that gives defenders governed access to frontier AI for cybersecurity work. For most security teams, Daybreak Blue, powered by GPT-5.6 Sol, serves as the starting point across defensive workflows including vulnerability discovery, detection engineering, and incident response. Daybreak Red, powered by a new GPT-5.6 Cyber, is designed for advanced, authorized tasks such as vulnerability research, exploit reproduction, and mitigation development. To enroll, contact OpenAI or reach out to your AWS account team for guidance on eligibility. To learn more, read the AI Blog post.
• New foundation models in Amazon SageMaker JumpStart: We’re expanding the portfolio of foundation models available to AWS customers. These models address different enterprise AI challenges with specialized capabilities:
  • NVIDIA’s Nemotron 3.5 Lightning model
  • NVIDIA’s Nemotron-Nano-12B-v2, Z.ai’s GLM-5.2 FP8, and GLM-OCR models
  • NVIDIA’s LocateAnything-3B, Qwen-AgentWorld-35B-A3B, and Qwen3.5-122B-A10B models
  • Black Forest Labs’ FLUX.2-small-decoder and Google’s gemma-4-12B-it models
  • Redis’s langcache-embed-v3-small, JetBrains’ Mellum2-12B-A2.5B-Thinking, and LightOn’s LightOnOCR-2-1B models

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional projects and news items you may find interesting:

• The deprecation of email validation in AWS Certificate Manager: ACM will discontinue support for email-validated public certificates by September 30, 2027. If you use email validation for your ACM public certificates, you need to migrate to DNS validation before that date. For Amazon CloudFront distributions, HTTP validation is also available.
• The next-generation AWS VPN Client with CLI support and admin controls: You can use a new AWS VPN Client built on OpenVPN3. With the new client, you get full backward compatibility with existing AWS Client VPN endpoints while delivering the automation capabilities and security posture that enterprise networking teams have been asking for.
• Oracle Exadata on Exascale for Oracle AI Database@AWS: ExaDB-XS brings Exadata-class performance and availability through a consumption-based model. With ExaDB-XS, you can scale compute and storage independently in small increments and pay only for what you consume.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events including AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

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

— Channy

This post was originally published on this site

When you move AI agents from prototype to production, the infrastructure challenges multiply. Your agents need to persist state across multi-step workflows that run for hours or days. They need to coordinate with other agents, share context, and sometimes access GPUs for specialized tasks. Amazon Bedrock AgentCore runtime microVMs provide a fully managed environment for invocations that can run for up to 8 hours and support stateful workflows through managed session storage. Some workloads also benefit from dedicated, larger-capacity environments — for example, when agents need to run continuously for multiple days, access GPUs or the underlying OS, or run multiple collaborating agents on the same host.

Today, I’m happy to announce runtime instances, a new complementary compute option in Amazon Bedrock AgentCore Runtime that gives your agents persistent, managed infrastructure purpose-built for complex agent workloads.

What you get
Runtime instances provides AWS-managed EC2 infrastructure where you deploy multiple agents in a single runtime, each with their own dependencies and artifact types. Your agents can collaborate on the same host within shared sessions that persist for up to 14 days. The service supports GPU acceleration for compute-intensive tasks, session stop/restart to save costs during idle periods, and containerized deployments for teams that want to ship independently. For knowledge that needs to survive beyond a session, runtime instances pairs naturally with Amazon Elastic Block Store (Amazon EBS) and AgentCore Memory, which gives your agents long-term recall across sessions and environments.

Before today, if you wanted to keep your agents running for days or they needed GPU access, or multi-agent coordination, you had to build and manage that infrastructure yourself. You provisioned EC2 instances, configured networking, set up session management, handled scaling, and stitched together monitoring. Runtime instances handles all of that for you while integrating with the same AgentCore APIs, identity controls, and observability you already use with AgentCore Runtime microVMs.

A few things that should make agent developers smile: your agents can call each other as tools within a shared session, iterating autonomously until the job is done. You bring any framework (CrewAI, LangGraph, LlamaIndex, Strands) and any model. Packaging is minimal, a @app.entrypoint decorator and a zip file or container image. And if your workflow spans days, hibernate Monday night and resume Wednesday morning with everything intact.

Runtime microVMs and runtime instances are complementary compute options that you can use independently or together through the same AgentCore runtime APIs. A lightweight orchestrator agent on runtime microVM can coordinate and dispatch work to specialized worker agents running on instances. The orchestrator handles API calls, task routing, and result aggregation using runtime microVM’s fast scaling, while workers on Instances perform compute-intensive tasks like code compilation, security scanning, or GUI automation that require persistent state and direct OS access.

Let me show you how it works
I built two agents for this demo: a code writer agent that generates Python code from natural language descriptions, and a code reviewer agent that analyzes the generated code for bugs, security issues, and style improvements. Both agents share the same file system, so the reviewer can read whatever the writer produces without any data transfer or API calls between them.

Here is the code writer (simplified, no error handling):

writer = Agent(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    system_prompt=(
        "You are a senior Python engineer. "
        "Given a task, return ONLY a single Python code block — no prose."
    ),
)

@app.entrypoint
def handler(event, context):
    task = event.get("task") or event.get("prompt")
    session_id = getattr(context, "session_id", None) or event.get("session_id")
    session_dir = SHARED_DIR / session_id
    session_dir.mkdir(parents=True, exist_ok=True)

    code = str(writer(task))
    (session_dir / "code.py").write_text(code)

    return {"agent": "writer", "wrote": str(session_dir / "code.py"), "code": code}

Here is the code reviewer agent (simplified, no error handling):

reviewer = Agent(
    model="us.anthropic.claude-sonnet-4-5-20250929-v1:0",
    system_prompt=(
        "You are a strict Python code reviewer. "
        "Given code, return 3 bullet points: bugs, style, suggestions."
    ),
)

@app.entrypoint
def handler(event, context):
    session_id = getattr(context, "session_id", None) or event.get("session_id")
    code_path = SHARED_DIR / session_id / "code.py"
    code = code_path.read_text()
    review = str(reviewer(f"Review this code:nn{code}"))

    return {"agent": "reviewer", "read": str(code_path), "review": review}

Each agent is a Python application using Strands Agents with an @app.entrypoint decorator and a model of its choice. I package each one as a zip file. For this demo, I use the AWS Management Console. You can also use the AgentCore CLI, the AWS Command Line Interface (AWS CLI) or infrastructure as code.

Step 1: Create a capacity provider.

A capacity provider defines the EC2 infrastructure your agents run on. In the AgentCore console, I select Runtime in the left navigation, then select the Capacity providers tab and Create capacity provider.

I give it a Name, select Linux (64-bit ARM) as the Operating system, and choose c7g.2xlarge as the Allowed instance types. This gives me 8 vCPUs and 16 GiB of memory, enough for both agents to run comfortably side by side.

Further down, I configure the VPC, subnets, and security groups for network access. Under Storage configuration, I keep the default gp3 volume. Under Service access, I select Create a new service role and let the console create the infrastructure role that manages EC2 instances on my behalf.

I select Create capacity provider and wait a few seconds. The status moves to Active.

Note the capacity provider configuration summary: operating system, instance type, subnets, security group, instance profile, and infrastructure role. Once created, only the description can be edited, so verify your settings before you proceed.

Step 2: Create a runtime and deploy the first agent.

Back on the Runtime page, I select Create runtime. I give it a Name, select Instances as the Compute type, and choose the Capacity provider I created in the previous step.

Under Agent source, I select S3 Source, then Upload to S3. I choose my agent zip file (ACIDemoWriter.zip), set the Language runtime to Python 3.13, and specify agent.py as the Agent entry point. This is the file that contains my @app.entrypoint decorated function. Under Permissions, I select Create default role to let the console provision the IAM role my agent needs.

I select Create runtime and wait for the status to become Ready.

I repeat the same process for my code reviewer agent. I create a second runtime, select the same capacity provider, upload my reviewer agent zip file, and wait for it to become Ready. Both agents now share the same underlying EC2 infrastructure.

The console shows me a View invocation code section with ready-to-use Python, TypeScript, and JavaScript snippets to invoke my agent programmatically. But for this demo, I use the built-in test feature. I select Test on the writer agent’s page.

Step 3: Invoke agents and observe collaboration.

The Runtime playground opens. At the top, I see three fields: Runtime agent, Endpoint, and Session ID. The console generates a session ID automatically. I take note of it because I will reuse it with the reviewer agent.

In the Input field, I type a JSON payload asking the writer agent to generate code:

{"prompt": "write a fibonacci suite"}

I select Run. After a few seconds, the Output panel shows the agent’s response. The writer agent generated a Python module with two implementations of a Fibonacci sequence (a list-based function and a generator) and wrote it to /tmp/agentcore-session/ca5ec24d-07f5-4eeb-add1-5ba416bf9eb2/code.py. Notice the session ID in the file path. That directory is the shared file system for this session.

Step 4: Invoke the reviewer agent in the same session.

Now I switch the Runtime agent dropdown to ACIDemoReviewer. The important part: I paste the same session ID (ca5ec24d-07f5-4eeb-add1-5ba416bf9eb2) in the Session ID field. This is what connects the two agents.

I type a simple prompt:

{"prompt": "review the code"}

I select Run. The reviewer agent reads the file the writer produced from the shared session directory and returns a detailed code review. It finds no critical bugs but suggests adding type hints, input validation, and simplifying the edge case handling.

The two agents never exchanged messages or called each other’s APIs. They collaborated through the shared file system that runtime instances provide within a session. You can extend this pattern to any number of agents: a test agent that runs the code, a documentation agent that generates README files, a security agent that scans for vulnerabilities, all sharing the same working directory.

Key details
Here are a few things to know as you get started:

• Supported OS: Linux (ARM64 and x86_64) at launch.
• Session persistence: Sessions persist for up to 14 days.
• Runtimes: Python 3.11-14 with native code support. Container images also supported.
• GPU: Support for GPU-accelerated instance types.
• Integration: Uses the same AgentCore APIs, identity, observability, and policy controls as AgentCore Runtime.
• Pricing: Standard EC2 pricing plus a management fee for AgentCore orchestration.
• Regions: US East (Ohio, N. Virginia), US West (Oregon), Asia Pacific (Mumbai, Singapore, Sydney, Tokyo), and Europe (Frankfurt, Ireland)

To get started, visit the runtime instance in Amazon Bedrock AgentCore documentation and create your first capacity provider.

— seb

This post was originally published on this site

Last week I had the joy of participating in Amazon’s “Bring Your Kids to Work Day” with my 7 year old son. We commuted together into the New York City office, his first real rush hour train ride, and spent the day exploring how Amazon uses AI, machine learning, and robotics to deliver packages to customers all over the world. Watching his eyes light up as he saw robots navigating a fulfillment center reminded me why so many of us got into technology in the first place. There’s nothing quite like seeing that sense of wonder when something complex clicks.

That same energy carried into the week’s launches. We’ve got updates across AI pricing, observability, multicloud networking, and data management. Let’s dive in.

Headlines
Amazon Bedrock announces up to 80% lower prices for OpenAI GPT‑5.6 models – If you’re using OpenAI’s GPT‑5.6 family through Amazon Bedrock, your costs just dropped significantly. Effective July 30, on-demand inference prices for GPT‑5.6 Luna are reduced by 80%, while GPT‑5.6 Terra prices are reduced by 20%. Luna now costs $0.20 per million input tokens and $1.20 per million output tokens, making it one of the most affordable frontier-class models available. These price reductions apply automatically — no action required on your part. Read more

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

• Amazon CloudWatch announces managed Prometheus collectors – Amazon CloudWatch now supports collecting Prometheus metrics from your AWS infrastructure using fully managed collectors, enabling you to monitor Amazon EKS, Amazon EC2, Amazon ECS, Amazon MSK, and Amazon OpenSearch Service workloads without deploying or managing any agents. If you’ve been maintaining your own Prometheus scraping infrastructure, this removes a significant operational burden. Read more
• AWS Interconnect — multicloud connectivity with Oracle Cloud Infrastructure is now generally available – AWS Interconnect is the first purpose-built multicloud connectivity product of its kind, allowing you to quickly provision resilient, scalable private connections between AWS and other cloud providers. With this GA launch for Oracle Cloud Infrastructure (OCI), you can establish private cross-cloud networking without traversing the public internet, making it easier to run multicloud architectures with the security and performance your workloads demand. Read more
• AWS IAM Identity Center extends multi-Region support to Identity Center directory – You can now replicate IAM Identity Center from your primary AWS Region to additional Regions when using the Identity Center directory as your identity source. If IAM Identity Center is affected by a disruption in the primary Region, your users continue to have access to their AWS accounts using provisioned entitlements in additional Regions. This feature was previously available only for instances connected to external identity providers. Read more
• Amazon S3 Tables now supports the Variant data type for Apache Iceberg V3 – Amazon S3 Tables adds support for the Variant data type, introduced in the Apache Iceberg V3 table format specification. Variant provides a high-performance, native solution for managing semi-structured data within your data lake — think IoT sensor data, application logs, and other schema-flexible payloads — without resorting to JSON blobs. Read more

Other AWS news
Here are some additional posts and resources that you might find interesting:

• Installing and updating the AWS CLI with single-line commands – A new blog post from the Developer Tools team that simplifies AWS CLI installation and updates across platforms with single-line commands. If you manage CLI versions across teams or in CI pipelines, this is a nice quality-of-life improvement.
• Deploying Kimi K3 on Amazon SageMaker HyperPod and Amazon EKS – A step-by-step guide for deploying Moonshot AI’s Kimi K3 model on AWS infrastructure using SageMaker HyperPod and Amazon EKS. If you’re evaluating large-scale model deployment options, this walks through the full workflow.
• Deliver Apache Kafka data to streaming tables for Apache Iceberg with Amazon MSK Express brokers – Learn how to stream data from Apache Kafka into Apache Iceberg tables using Amazon MSK Express brokers, with throughput support of up to 10 GB/s for delivery to Apache Iceberg on Amazon S3 Tables.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

• AWS Summits – AWS Summits are free events that bring the cloud and AI community together to connect, learn, and explore the latest technologies. Browse the full calendar to find a Summit near you in the second half of 2026.
• AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

---

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

This post was originally published on this site

Last week I had the privilege of spending three days in São Paulo with technical builders from across Latin America, brought together for a regional tech event full of deep-dive sessions, hands-on workshops, and conversations with customers and partners. What struck me most wasn’t any single session, it was the energy of a technical community that so rarely gets to be in the same room. People traded architecture ideas over coffee, sketched out solutions on whiteboards, and left with a longer list of things to try than they arrived with. It’s a good reminder that, for all the tooling we build, the community around it is what makes the technology stick.

That community spirit connects nicely to the week’s biggest infrastructure news, which is all about bringing AWS closer to where builders actually are.

Now, let’s get into this week’s AWS news…

Headlines
AWS Local Zone in Athens, Greece: AWS has opened a new Local Zone in Athens, Greece, the second Local Zone in EMEA with support for Amazon S3 and Amazon EBS Local Snapshots, so you can store and process data within Greece to help meet local data residency requirements. The Athens Local Zone supports Amazon EC2 (C7i, M7i, and R7i instances), Amazon S3 with the One Zone-Infrequent Access storage class, Amazon EBS, and Amazon ECS.

AWS Local Zones place AWS infrastructure much closer to large population and industry hubs, enabling applications that require single-digit millisecond latency, such as real-time gaming, media production, and financial services, to run where end users actually are. For builders in Greece, you can now run latency-sensitive workloads locally while connecting seamlessly to the nearest AWS Region for services that don’t require low latency, giving you the flexibility to architect hybrid, latency-optimized applications without managing your own data center infrastructure. To learn more, visit AWS Global Infrastructure and Sustainability Blog post.

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

• Claude Opus 5 on AWS: You can use Anthropic’s Claude Opus 5, the most advanced Opus model yet, matching Claude Fable 5’s top-tier intelligence in many domains at Opus-tier pricing. Amazon Bedrock offers Claude Opus 5 with zero data retention (ZDR) enabled by default, giving you Opus’ top-tier intelligence while meeting your data governance requirements unlike Claude Fable 5. You have two ways to access Claude Opus 5: Amazon Bedrock and Claude Platform on AWS. To learn more, visit the deep dive blog post.
• AWS Lambda durable execution SDK for .NET is now generally available: You can now build resilient, long-running workflows in C# using Lambda durable functions, without implementing custom progress tracking or integrating an external orchestration service. The SDK is a natural fit for multi-step applications like payment processing pipelines, AI agent orchestration, and human-in-the-loop approvals, it checkpoints progress automatically and can pause execution for up to a year. If you’re a .NET developer building serverless workflows, this removes a lot of the plumbing you used to write by hand.
• Amazon Bedrock AgentCore now delivers unified observability with traces and logs in a single log group: Amazon Bedrock AgentCore now delivers agent traces and prompts to the same Amazon CloudWatch log group as your agent’s logs. Previously, telemetry was split across destinations, trace spans went to a shared log group while prompts, inputs, and outputs went to a separate one, so debugging a single agent invocation meant searching in multiple places. You can now debug an invocation in one place, and apply fine-grained access control and customer-managed key (CMK) encryption at the individual agent level.
• Amazon Connect delivers more natural agentic voice experiences: Amazon Connect now supports more natural, human-sounding agentic voice experiences across 50+ languages, including Portuguese, Spanish, French, Italian, Japanese, Korean, and Thai, with over 100 new voice options and conversational improvements that make AI interactions sound more fluid. Connect’s agentic self-service lets AI agents understand, reason, and take action across voice and digital channels, adapting to a customer’s tone and sentiment. You can now build contact center experiences that feel natural to callers in far more of the languages your customers actually speak.
• Amazon SageMaker Unified Studio now supports Amazon OpenSearch: You can now query and analyze your search and log analytics data from Amazon OpenSearch directly alongside other data assets in Amazon SageMaker Unified Studio. With this connection, you can combine operational search data in OpenSearch with data from sources like Amazon Redshift, Amazon S3, and relational databases, all within a single, governed environment. It’s especially useful when you need to correlate analytical and operational workloads, such as joining application logs with transactional data to uncover insights.
• Amazon CloudWatch announces coding agent insights: Amazon CloudWatch now gives engineering leaders visibility into how AI coding tools are driving value across their organization. Coding agent insights integrates with the Claude apps gateway for AWS to collect telemetry from Claude Code without additional instrumentation, and also supports agents like Codex and GitHub Copilot. As teams scale AI coding adoption, you can now measure the return on that investment with metrics built on OpenTelemetry, no custom instrumentation required.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Other AWS news
Here are some additional posts and resources that you might find interesting:

• Evaluating AI Agents: A production blueprint with Strands and AgentCore: A practical guide to evaluating AI agents before and after they reach production, using Strands Agents and Amazon Bedrock AgentCore. If you’re moving agents from prototype to production, this post is a great companion to the AgentCore observability update above, it walks through how to measure agent quality systematically rather than by gut feel.
• Building multi-region resiliency for AWS CloudFormation custom resource deployment: Learn how to architect CloudFormation custom resources for multi-region resiliency, so your infrastructure-as-code deployments stay reliable even when a single Region has issues.
• Introducing Amazon Simple Email Service (SES) pricing plans: Amazon SES now offers pricing plans that give you more predictable costs as your email volume grows. If you send at scale, this could simplify your billing significantly.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

• AWS Summits: AWS Summits are free events that bring the cloud and AI community together to connect, learn, and explore the latest technologies. Browse the full calendar to find a Summit near you in the second half of 2026.
• AWS Community Days: Community-led conferences where content is planned, sourced, and delivered by community leaders. If you’re in Latin America, don’t miss AWS Community Day Belo Horizonte on August 22, registration is open at awscommunityday.com.br.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

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

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

This post was originally published on this site

Last week, my team visited Seoul to meet AWS Korea User Group (AWSKRUG) leaders. AWSKRUG is the largest cloud developer community in Korea, with 20 meetup groups organized by topic and area that collectively host over 100 events each year, primarily in Seoul.

My team regularly visits countries across the Asia-Pacific region, listens to feedback from user group leaders, and works to support their communities. At this meeting, leaders honestly shared what they did well in the first half of the year, what needs improvement, and what they asked of AWS Developer Experience team. We also enjoyed a pleasant conversation during our Chimaek time together.

Now, let’s take a closer look at key launches of last week.

A one-click Lambda setup prompt for coding agents caught my eye most last week. This prompt configures your agent with AWS Serverless skills and the Serverless Model Context Protocol (MCP) server, embedding serverless best practices from the start. This prompt references the Lambda agent setup guide, which includes installation commands for Claude Code, Kiro, Cursor, GitHub Copilot, Codex, Devin Desktop, and OpenCode.

To get started, choose the Copy agent prompt button on the Lambda console screen or copy fetch https://docs.aws.amazon.com/lambda/latest/dg/samples/aws-lambda-agent-setup.md directly, and paste this URL in your preferred AI agent.

You can also use Agent Toolkit for AWS to give your coding agent current AWS knowledge and safe resource access. Use fetch https://raw.githubusercontent.com/aws/agent-toolkit-for-aws/refs/heads/main/setup-instructions/setup.md for installing AWS MCP Server.

Last week’s launches
Here are last week’s launches that caught my attention:

• OpenAI GPT-5.6 Sol, Terra, and Luna on Amazon Bedrock: You can use the smartest family of models from OpenAI yet on Bedrock’s next-generation inference engine built for high performance, security, and reliability. The three models span capability tiers from flagship reasoning (Sol) to balanced performance (Terra) to fast, cost-efficient inference (Luna), all accessible through the Responses API on Amazon Bedrock.
• Same-day transitions to Amazon S3 Standard-IA and S3 One Zone-IA: You can now transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) and S3 One Zone-Infrequent Access (S3 One Zone-IA) as soon as the day they are created, without the previous 30-day minimum retention period in S3 Standard. These storage classes offer up to 40% lower storage costs than S3 Standard while still providing millisecond access when needed, making them ideal for backups, log analytics, and compliance workloads where data becomes cold within hours or days.
• Self-managed code storage on AWS Lambda: With self-managed Amazon S3 buckets for code storage, you can reference source code directly from your own S3 buckets without Lambda creating intermediate copies. This eliminates code storage limits and reduces function activation time after function creates and updates by removing the copy step.
• Importing users with password hashes on Amazon Cognito: You can now import users with password hashes in CSV user imports. Previously, imported users had to reset their passwords on first sign-in. Now, you can include password hashes in the CSV import, enabling users to sign in immediately with their existing credentials. When creating a CSV import, you specify the password hashing algorithm used by your source system.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Additional updates
Here are some additional news items that you might find interesting:

• Amazon SQS turns 20: Two decades of reliable messaging at scale: When Amazon SQS launched publicly in July 2006, it made this pattern available to every AWS customer. Twenty years later, that core function, decoupling producers from consumers, remains the reason customers use SQS. Let’s look back important milestones after Jeff’s 15th anniversary post.
• Open Protocols with the Strands Agents SDK: Learn how open AI protocols such as MCP, A2A, UTCP, AG-UI, and x402 work together using Strands Agents SDK for building AI agents as an example implementation, though the patterns apply to any agent framework.
• Open source Bulk Executor for Amazon DynamoDB: Performing bulk operations against all items in a DynamoDB table has historically required custom coding. The Bulk Executor for DynamoDB simplifies bulk tasks like these. You can use this feature to invoke commands like count, find, delete, or update. No coding is required, even when running at large scale.
• Transform AWS Support Case Workflows with Kiro CLI: Explore how Kiro CLI’s MCP integration accelerates support case workflows by combining investigation, documentation lookup, and case creation into a single conversational interface across three real-world scenarios: AWS Glue job failures, AWS Lambda cold start investigation, and AWS WAF false positive analysis.

For a full list of AWS blog posts, be sure to keep an eye on the AWS Blogs page.

Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events including AWS Summits. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

Finally, some customers experienced an issue with Cost Explorer displaying inaccurate estimated billing data in last weekend. They may have received erroneous budget and cost anomaly detection alerts, and observed inflated estimated cost and usage data. The issue has been resolved, and all AWS services are operating normally. We apologize for the concern this incident caused our customers and are conducting a thorough retrospective to prevent events like this from reoccurring, as well as improving our response when billing incidents occur. For more information, visit the AWS Health Dashboard.

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

— Channy

This post was originally published on this site

A couple of editions ago I wrote about what I find so energizing about working with startups. Last week I got a fresh dose of it: I spent a few days with the AWS Startups team, listening to stories of founders talking about the problems they’re actually solving. One story that stayed with me came from Marco Negreiros, founder of EyeCare Health, a Brazilian healthtech expanding access to eye care. He shared a striking fact: more than 70% of Brazilian municipalities don’t have a single ophthalmologist. His answer was to put a vision test on the one device almost everyone already carries, the smartphone, so a basic eye screening no longer depends on living near a clinic. Watching a founder turn a gap that big into something that concrete is exactly why I love this space.

This week, I’ll take a closer look at some key launches, and then cover the quarterly AWS Service Availability updates.

Last week’s launches
Here are some of the launches covered from this past week in the AWS News Blog:

• Amazon EC2 C9g and C9gd instances powered by AWS Graviton5 processors: They deliver up to 25% better compute performance than Graviton4-based instances, 5x larger cache, fastest memory of any processor instances in the cloud, and local NVMe storage options (C9gd).
• A new AWS CloudFormation Express mode: You can speed up infrastructure deployment with AWS CloudFormation Express mode, enabling AI agents and developers to receive deployment confirmation in seconds and iterate faster. Available in all commercial Regions at no additional cost.
• Upgrade Amazon EKS clusters with confidence using Kubernetes version rollbacks: Learn how Kubernetes version rollbacks for Amazon EKS let you reverse cluster upgrades within seven days. This new feature provides a safety net for upgrade failures, no cluster rebuilds required, turning Kubernetes version upgrades into a reversible, low-risk operation.
• Automate public TLS certificate issuance with ACME support in AWS Certificate Manager: AWS Certificate Manager now supports the ACME protocol, so you can automate the issuance and renewal of public TLS certificates using standard, widely adopted tooling.

Here are some launches and updates that caught my attention:

• Claude Sonnet 5 is now available on AWS – Anthropic’s most capable Sonnet model brings top-tier intelligence at Sonnet pricing for coding, agents, and everyday professional work at scale. It navigates large codebases, calls tools precisely, and holds state across long agentic tasks. To learn more, visit the AI Blog post.
• Amazon WorkSpaces for AI agents is now generally available: AI agents can now securely access and operate desktop applications through managed WorkSpaces environments, without requiring application modernization or custom integrations. To learn more. visit the Desktop and Application Streaming Blog post.
• Amazon OpenSearch Service is now optimized for log analytics: This release introduces a new engine purpose-built for log analytics workloads that delivers up to 4x better price-performance on internal benchmarks, while keeping the full-text search capabilities OpenSearch is known for. Teams can now get aggregations and precise text search in one place. To learn more, visit the Big Data Blog post.
• Amazon SageMaker AI cuts generative AI inference scale-out time by up to half: SageMaker Inference now supports container image caching, enabling up to 2x faster end-to-end scaling for generative AI models during scale-out events. To learn more, visit the AI Blog post.
• Amazon CloudWatch supports creating alarms from log queries : You can now create alarms directly on log query results and set thresholds in a single workflow, eliminating the need to first create metric filters or custom metrics as intermediate steps.

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

AWS Service Availability Updates
When the availability of an AWS service or feature changes, we provide customers guidance in AWS Product Lifecycle Changes on available alternatives and support for migration so that disruptions to your operations are minimized. The following lifecycle changes were updated on June 30, 2026.

Services moving to Maintenance (no longer accessible to new customers starting July 30, 2026):

• Amazon Bedrock Agents (launched November 2023) is now Amazon Bedrock Agents Classic
• Amazon Cognito Sync
• Amazon Kendra
• Amazon Q Business
• AWS Directory Service – Simple AD
• AWS IoT Device Defender – Detect (feature will no longer be accessible to new customers starting August 31, 2026)
• AWS Mainframe Modernization – Self-Managed Experience
• AWS Management Console – myApplications
• AWS Resource Groups – Group Lifecycle Events
• AWS Service Catalog – Application Registry
• AWS Systems Manager – Application Manager
• Amazon SageMaker AI features: A2I, Clarify, Debugger, GeoSpatial, Ground Truth, Mechanical Turk, Model Monitor, Role Manager, and Studio Lab

Services entering Sunset:

• Amazon WorkSpaces – PCoIP
• Amazon WorkSpaces – Pool
• AWS Managed Services (AMS) Advanced
• AWS re:Post Private
• Amazon Sagemaker AI- Profiler

Services reaching End of Support (as of June 30, 2026):

• Amazon Chime SDK – Carrier Voice Focus
• Amazon SageMaker AI – Ground Truth Plus

We understand that changes in availability can impact your operations. For specific guidance, consult the relevant service documentation or contact AWS Support.

Upcoming AWS events
Check your calendar and sign up for upcoming AWS events:

• AWS Summits – AWS Summits are free events that bring the cloud and AI community together to connect, learn, and explore the latest technologies. Browse the full calendar to find a Summit near you in the second half of 2026.
• AWS Community Days – Community-led conferences where content is planned, sourced, and delivered by community leaders. If you’re in Latin America, don’t miss AWS Community Day Belo Horizonte on August 22. Registration is open at awscommunityday.com.br.

Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development. Browse here for upcoming AWS-led in-person and virtual events and developer-focused events.

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

– Daniel Abib

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

This post was originally published on this site

Upgrading a Kubernetes control plane has long been a one way door. Open source Kubernetes doesn’t support control plane rollback, so once you upgrade, there’s no going back. The community is making real progress here, and KEP-4330 introduces emulated versions to ease rollback. But in practice this constraint has pushed organizations to build elaborate compensating mechanisms like bake periods, stagger groups, automated sign offs, and months long upgrade cycles. With Kubernetes releasing three minor versions per year, teams managing hundreds of clusters, especially in regulated environments, often delay upgrades entirely because they aren’t confident they can recover if something goes wrong. The result is clusters stuck on older versions, missing security patches, and eventually running up against extended support timelines.

Today, we’re announcing Kubernetes version rollbacks for Amazon Elastic Kubernetes Service (Amazon EKS), a new feature that gives cluster administrators a safety net when performing cluster upgrades. With version rollbacks, you can reverse a Kubernetes version upgrade within seven days if you encounter issues after upgrading, returning your cluster to its previous working state.

Where approaches like emulated versions keep a cluster in a transitional holding state, EKS version rollback returns your cluster to a fully validated previous version that ran in production, not an emulation of it. Now, if you upgrade a cluster from, say, Kubernetes 1.34 to 1.35 and discover a compatibility issue, you can roll back to 1.34 within seven days. There’s no need to rebuild your cluster or scramble to troubleshoot under pressure. Think of it as an undo button for Kubernetes version upgrades.

The feature supports rolling back one minor version at a time, matching the same incremental approach EKS uses for upgrades. And to help you roll back safely, EKS automatically evaluates your cluster’s rollback readiness through cluster insights, flagging items like node version compatibility or add-on dependencies before you proceed. If you’ve already assessed the situation and want to move quickly, you can use the --force flag to bypass those checks. The above applies to all EKS clusters, whether you manage your own nodes or let AWS handle them. But for customers who have embraced fully managed infrastructure, rollback goes a step further.

Rollback for EKS Auto Mode
EKS Auto Mode gives you one click deployment of production ready Kubernetes clusters, automating compute, networking, and storage management so you can focus on your applications rather than infrastructure. EKS Auto Mode introduces additional considerations for version rollbacks because both the control plane and managed nodes need to be rolled back together. Since node rollbacks respect your pod disruption budgets, the process can take time depending on your configuration.

To give you control over this process, we’ve introduced a cancel API that lets you stop a node rollback at any point. If you decide the rollback is taking too long or you want to change your approach, you can cancel and adjust your disruption budgets to accelerate things, or choose a different path forward.

By default, EKS never bypasses your disruption budgets during a rollback because we prioritize workload stability. You can always choose to modify or remove disruption budgets yourself to speed up the process if needed.

Let’s try it out
To try version rollbacks, I navigated to the Amazon EKS console and selected one of my clusters that I had recently upgraded.

From the cluster’s configuration page, I can see the option to initiate a version rollback, along with information about my current rollback window.

Before initiating the rollback, I reviewed the rollback insights to check for any potential issues. The insights showed me the status of my nodes and flagged anything I should address before proceeding.

After confirming, the rollback began. My cluster remained functional throughout the process. The control plane rollback took about 20 minutes, similar to a standard upgrade. For my EKS Auto Mode cluster, the nodes rolled back gracefully according to my disruption budget settings.

Once complete, my cluster was back on the previous Kubernetes version, running as expected.

Now available
Kubernetes version rollbacks for Amazon EKS are available today at no additional cost in all commercial AWS Regions where Amazon EKS is available. You pay only for the standard EKS and compute costs you would normally incur. There are no extra charges for using the rollback capability.

Control plane rollbacks are available for all EKS clusters, and node rollbacks are available for clusters running EKS Auto Mode. Version rollbacks support clusters running Kubernetes versions available in EKS standard support and extended support.

To get started, visit the Amazon EKS documentation or try it out directly in the Amazon EKS console.