Nothing last week added a new kind of compute. What it did instead was shorten the distance between the code you already have and that code being live. AWS Lambda learned to run a plain HTTP server with no adapter in front of it, and Amazon S3 became something you can provision from inside the tool you were already using, without opening the AWS Management Console. Different services, same direction.

The counterweight arrived on Thursday. As it gets easier to put things into production, it matters more that you can say what those things are allowed to do, and this week’s most interesting read is about exactly that. Now, let’s get into this week’s AWS news…

Headlines
AWS Lambda Web Functions is a new form factor that runs a standard HTTP web application on Lambda without rewriting it into an event handler. You take a port-listening server built with Express, Hono, Fastify, or Next.js, run aws lambda-web deploy, and get back a live HTTPS URL in under a minute. No adapters, no infrastructure to set up first, no framework lock-in. Lambda now has three form factors on the same Firecracker isolation, which compose inside one application: Event Functions (the model you already know) for async work, Web Functions for the user-facing layer, and MicroVMs for stateful sandboxes running untrusted code.

Two details make this more than repackaging. Each execution environment handles up to 128 concurrent requests, so a burst of traffic shares warm environments instead of mapping one request to one environment, and billing meters the CPU time your code actually uses rather than wall-clock time. For server-side rendering, API backends, and inference endpoints streaming tokens, where most of the elapsed time is spent waiting, that changes the arithmetic. Adding --regions to the same deploy command replicates your code and configures latency-based routing behind a single global URL, and you can split traffic across revisions by weight for a canary rollout. It’s available in five Regions, running Node.js 24 on Arm64 with VPC connectivity. The AWS Compute Blog has a longer walkthrough, and Amazon CloudFront can now use a Web Function endpoint as an origin with origin access control.

The week’s other headline isn’t a launch. Trust in Depth: giving agents more autonomy without losing control makes an argument I keep running into in practice: putting a person in the loop for every agent action works at small scale and then quietly stops working, because people asked to check everything stop catching what matters. The alternative is four questions answered at four moments, which AWS calls scope, know, bound, and observe. The part worth sitting with is that an agent’s limits belong outside its own reasoning, in Amazon Bedrock AgentCore policies enforced at the gateway, because an agent cannot turn off a control it does not run. AWS has run automated reasoning in production for more than a decade, including AWS Identity and Access Management at more than 2.1 billion requests per second, and the case here is for pointing it at the rules you write for agents.

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

• Amazon S3 is now available on the Vercel Marketplace and v0 by Vercel – You can now provision S3 general purpose buckets, S3 Tables, and S3 Vectors from the Vercel dashboard, or by describing what you want to build in v0. It joins existing integrations for Amazon Aurora, Amazon DynamoDB, and Amazon OpenSearch Serverless, so the whole stack sits in one AWS account with one bill. The authentication detail is the good part: a short-lived OIDC token is exchanged at runtime with AWS Security Token Service for credentials scoped to that resource, so no static keys are stored anywhere.
• Amazon EC2 T8i instances are now available in South America (São Paulo) – The newest burstable generation reached the São Paulo Region, which matters for the unglamorous half of a workload: login gateways, small databases, CI/CD runners, low-traffic sites. Amazon EC2 T8i delivers up to 30 percent better price performance than T3, with up to 70 percent higher compute performance and 2.4 times the Amazon EBS bandwidth. It keeps the same CPU credit system as T3, so moving over is a size change rather than a redesign, and t8i.micro and t8i.small are available under the AWS Free Tier.
• Reflection Beam, an open-weight model for agentic coding, is now on AWS – You can now deploy Beam, Reflection AI’s open-weight model for agentic coding and long-horizon tasks, from Amazon SageMaker JumpStart. It’s a sparse mixture-of-experts model with 501 billion total and 23 billion active parameters, released on Hugging Face under Apache 2.0, and Reflection reports an effective context length of 1 million tokens. Start with a JumpStart real-time endpoint, or use SageMaker HyperPod Inference with SGLang or vLLM when you need Kubernetes-native control. It was also ported and optimized for AWS Trainium3, and launches in N. Virginia and Ohio.
• AWS Lambda Durable Execution SDK for Go is now generally available – You can now write multi-step Go workflows that checkpoint their own progress and pause for up to a year while waiting on an external event, without building progress tracking yourself. That covers deployment rollouts with approval gates and pipelines that call a model and then stop for human review. It behaves like ordinary Go tooling: go get to add it, deploy a single static binary on the provided.al2023 runtime, and test a full workflow locally with the durabletest package, no network access or credentials required.
• Amazon CloudWatch announces log routing and chained log parsers – You can now define routes in an Amazon CloudWatch pipeline that send each event to a different log group based on conditions evaluated against processed fields, so severity, source, or event type can each land somewhere with its own retention and access controls. Chained parsers handle the other half: parsers run in sequence, with later ones consuming fields that earlier ones produced, so you can parse a JSON envelope first and then run a Grok parser over a field it extracted.

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:

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

• AWS re:Invent – re:Invent returns to Las Vegas from November 30 to December 4, with more than 2,200 sessions. The event catalog is live and reserved seating is open, so register and claim your spots before they fill up.
• AWS Community Days – Community-led conferences planned and delivered by community leaders. Here in Brazil, the 2026 season closes with AWS Community Day Brasil Norte in Belém on October 31, the final stop in a year that covered every region of the country for the first time.

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!

The post AWS Weekly Roundup: AWS Lambda Web Functions, Amazon S3 on the Vercel Marketplace, and more (October 19, 2026) appeared first on The NOKIA Blog.

Developers building web and AI applications on Vercel can now add Amazon S3 storage resources directly from the Vercel Marketplace and v0, combining the durability, scalability, and performance of S3 with the convenience of Vercel’s provisioning workflow. Starting today, Amazon Simple Storage Service (Amazon S3), including Amazon S3 Tables and Amazon S3 Vectors, is natively available on the Vercel Marketplace and v0 by Vercel, covering three distinct workloads: everyday storage, analytics, and AI.

Why this matters
Millions of developers build, deploy, and scale web and AI applications on Vercel. Storage is essential to performant applications, but provisioning it previously required navigating to the AWS Management Console to configure policies and credentials. Now, S3 storage integrates directly into the Vercel developer experience, with resource provisioning and connection configuration handled automatically. Together with existing integrations for Amazon Aurora, Amazon DynamoDB, and Amazon OpenSearch Serverless, you get a complete application stack inside Vercel (from your database layer through to object, vector, and open table storage), all on one AWS account with one bill.

Storage for every application

• S3 general purpose buckets: Store virtually unlimited objects, including files, images, videos, and documents, for data lakes, cloud-native applications, and mobile apps. S3 delivers the most durable storage in the cloud, designed to provide 99.999999999% (11 nines) data durability and 99.99% availability by default.
• S3 Tables: Fully managed Apache Iceberg storage optimized for analytics with automated table maintenance and compaction. S3 Tables are directly queryable from any Iceberg REST catalog API-compatible engine, including DuckDB.
• S3 Vectors: Purpose-built vector storage and query for AI applications such as retrieval-augmented generation (RAG) and semantic search, reducing the cost of uploading, storing, and querying vectors by up to 90%.

Figure 1 – The AWS page on the Vercel Marketplace.

Getting started
From the Vercel dashboard, go to the Storage tab and choose Create. Select AWS, pick your S3 bucket type, choose a Region, and create the bucket. Provisioning takes a few seconds.

Figure 2 – Creating an Amazon S3 general purpose bucket in Vercel by choosing a Region and a name.

Connect the bucket to a project, then pull the environment variables with the Vercel CLI:

vercel link 
vercel env pull .env.local

The integration injects AWS_REGION, AWS_ROLE_ARN, AWS_RESOURCE_ARN, and a bucket name such as S3_BUCKET_NAME, S3_TABLE_BUCKET_NAME, or S3_VECTOR_BUCKET_NAME. At runtime, the awsCredentialsProvider from @vercel/functions/oidc exchanges a short-lived OIDC token with AWS Security Token Service (AWS STS) for temporary credentials scoped to your resource. No static keys are stored anywhere.

Object storage with S3
This Next.js route uploads a file to a general purpose bucket:

import { S3Client, PutObjectCommand } from "@aws-sdk/client-s3";
import { awsCredentialsProvider } from "@vercel/functions/oidc";

const s3 = new S3Client({
  region: process.env.AWS_REGION,
  credentials: awsCredentialsProvider({
    roleArn: process.env.AWS_ROLE_ARN,
  }),
});

export async function POST(request) {
  const formData = await request.formData();
  const file = formData.get("file");

  await s3.send(new PutObjectCommand({
    Bucket: process.env.S3_BUCKET_NAME,
    Key: file.name,
    Body: Buffer.from(await file.arrayBuffer()),
    ContentType: file.type,
  }));

  return Response.json({ success: true });
}

Use ListObjectsV2Command and GetObjectCommand with the same client to list and read objects.

Analytics with S3 Tables
S3 Tables offer S3 storage purpose-built for analytics and are designed to automatically optimize query performance as data volumes grow. They provide fully managed Apache Iceberg tables with automated table maintenance and compaction, and they work with any Apache Iceberg REST Catalog-compliant engine. You can use DuckDB inside a Vercel Function for lightweight queries. This example attaches the table bucket by its ARN and runs an aggregate query:

import { DuckDBInstance } from "@duckdb/node-api";
import { awsCredentialsProvider } from "@vercel/functions/oidc";

const creds = await awsCredentialsProvider({
  roleArn: process.env.AWS_ROLE_ARN,
})();

const db = await DuckDBInstance.create();
const conn = await db.connect();

await conn.run("INSTALL aws; LOAD aws; INSTALL httpfs; LOAD httpfs; INSTALL iceberg; LOAD iceberg;");

await conn.run(`
  CREATE SECRET s3cred (
    TYPE s3,
    KEY_ID '${creds.accessKeyId}',
    SECRET '${creds.secretAccessKey}',
    SESSION_TOKEN '${creds.sessionToken}',
    REGION '${process.env.AWS_REGION}'
  );
`);

await conn.run(`
  ATTACH '${process.env.AWS_RESOURCE_ARN}' AS s3tables (
    TYPE iceberg,
    ENDPOINT_TYPE s3_tables
  );
`);

const rows = await (await conn.run(`
  SELECT town_city, AVG(median_price) AS avg_price
  FROM s3tables."${process.env.S3_TABLES_NAMESPACE}".town_monthly
  GROUP BY town_city
  ORDER BY avg_price DESC
  LIMIT 10
`)).getRowObjects();

If you run DuckDB inside a Next.js route, add @duckdb/node-api to serverExternalPackages in next.config.mjs so the native bindings are not bundled.

Vector search with S3 Vectors
S3 Vectors deliver purpose-built, cost-optimized vector storage for AI agents, AI inference, and semantic search of your content stored in Amazon S3. S3 Vectors provides native support for storing and querying vectors without a separate vector database, but it does not generate them. Create the embeddings with your model of choice, then query the index for the closest matches:

import { S3VectorsClient, QueryVectorsCommand } from "@aws-sdk/client-s3vectors";
import { awsCredentialsProvider } from "@vercel/functions/oidc";

const vectors = new S3VectorsClient({
  region: process.env.AWS_REGION,
  credentials: awsCredentialsProvider({
    roleArn: process.env.AWS_ROLE_ARN,
  }),
});

export async function POST(request) {
  const { embedding } = await request.json();

  const result = await vectors.send(new QueryVectorsCommand({
    vectorBucketName: process.env.S3_VECTOR_BUCKET_NAME,
    indexName: "documents",
    queryVector: { float32: embedding },
    topK: 5,
    returnMetadata: true,
  }));

  return Response.json({ matches: result.vectors });
}

Building with v0 by Vercel
From v0, describe what you want to build and it provisions the right resource. Ask for “a photo gallery where users upload images using AWS” to get a general purpose bucket, “a support bot that answers questions from my PDFs” to get S3 Vectors, or “an analytics dashboard for my clickstream data” to get S3 Tables. v0 then configures the connection and generates working code.

Figure 3 – v0 generating a PDF support bot from a natural language prompt, with Amazon S3 Vectors connected and the application in the Preview panel.

Combining S3 with AWS databases
Every AWS service on Vercel shares the same account and authentication model, so you can combine them with no extra setup. For example, store file metadata in Aurora with the files in S3, or store document text in DynamoDB with embeddings in S3 Vectors for RAG retrieval.

Using Vercel with your AWS account
You can create a new AWS account through the integration, which supports S3 features for building and testing. You can also link an existing AWS account to use your full set of S3 and AWS capabilities and keep your current billing. If you link an existing AWS account, Vercel works only with new buckets that you create through the platform. Because every S3 resource lives in your own AWS account, you keep AWS-native tooling and a single place to manage storage.

Pricing and availability
There is no charge for the integration, and you pay standard Amazon S3 pricing for the resources you use. New AWS accounts created through Vercel receive $100 in credits valid for six months. Amazon S3, including S3 Tables and S3 Vectors, is available from v0 and the Vercel Marketplace in all commercial AWS Regions supported by Vercel. To get started, visit v0 or the AWS page on the Vercel Marketplace.

Join the conversation
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. Share your feedback on AWS re:Post for Amazon S3.

— Daniel

The post Amazon S3 is now available on the Vercel Marketplace and v0 by Vercel appeared first on The NOKIA Blog.

Some numbers are hard to picture until you sit with them for a moment. Last week we published the Prime Day 2026 metrics, and the one that stuck with me is 2.3 quadrillion: the daily requests Amazon ElastiCache peaked at during the event. Prime Day ran back in June, so this post always lands as a retrospective, and that gap is part of what makes it worth reading. By the time the numbers reach you, the only evidence that any of it happened is that nothing went wrong.

The rest of the week pulled in a different direction: smaller, cheaper, and more constrained. Claude Haiku 5.5 arrived on Amazon Bedrock at roughly 75 percent less than Haiku 4.5, Amazon Nova 2.5 Sonic made voice agents quicker on their feet, and Strands Box showed up to put real boundaries around what an agent is allowed to do. Now, let’s get into this week’s AWS news…

Headlines
All the numbers: Amazon Prime Day 2026 powered by AWS is the latest entry in a tradition that goes back to 2016, and the scale is still difficult to hold in your head. Over the four days of Prime Day, Amazon DynamoDB processed more than 59 trillion requests while peaking at 192 million requests per second, and kept single-digit millisecond responses throughout. AWS Lambda handled over 2.3 trillion invocations per day. Amazon EBS peaked above 24.8 trillion I/O operations, moving more than an exabyte of data daily. Amazon ECS launched an average of 158.3 million tasks per day on AWS Fargate, a 47.7 percent jump over last year, and AWS Graviton powered up to 49 percent of the Amazon EC2 compute that Amazon.com used.

The number I keep coming back to, though, isn’t a throughput record. The team ran more than 44,000 AWS Fault Injection Service experiments ahead of the event, more than six times what they ran in 2025. Those are deliberate failures injected on purpose, months in advance, so that the trillions of requests above turn into an ordinary shopping afternoon. If you’re preparing for your own peak moment, AWS Countdown Premium is built for exactly that kind of planning.

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

• Claude Haiku 5.5 is now available on AWS – You can now use the fastest model in the Claude 5.5 family on Amazon Bedrock and Claude Platform on AWS, at around 75 percent less than Claude Haiku 4.5 for most tasks. It’s the first Haiku with effort controls, so you can tune cost against intelligence per task instead of fixing one setting for a whole workload. It pairs naturally with Claude Opus 5.5: Opus plans and makes the judgment calls, and because Haiku is fast and cheap, you can run many Haiku subagents in parallel for routing, classification, and small code changes across many files.

• Amazon Nova 2.5 Sonic for voice agents – You can now build real-time voice agents on a speech-to-speech Amazon Nova model with better reasoning, instruction following, and tool-calling accuracy, plus lower latency. That combination matters for multi-step work: a support agent can look up an order, check return eligibility, and start the return inside one conversation. It keeps expressive voices in seven languages, controllable turn-taking, and a 256K context window, and pairs with Strands Bidi Agents, now generally available, to get a production voice agent going in a few lines of code.

• Amazon GuardDuty RDS Protection now detects data exfiltration and destruction – Amazon GuardDuty now looks past login anomalies to the queries themselves on Amazon Aurora PostgreSQL and Amazon RDS for PostgreSQL. RDS Protection for data activity builds behavioral baselines and uses ML-based anomaly detection plus threat intelligence to flag unusual data access, with no agents, extra infrastructure, or database configuration changes. This closes a real gap: once an attacker has valid credentials from phishing or a misconfiguration, their queries can look indistinguishable from your application’s.

• AWS Lambda supports OAuth for self-managed Apache Kafka event sources – You can now authenticate Lambda Kafka consumers through your enterprise identity provider, such as Amazon Cognito or Okta. Event source mappings previously supported only SASL/PLAIN, SASL/SCRAM, and mTLS, which left teams in regulated industries unable to use Kafka ESM at all. Now you get the automatic scaling, batching, error handling, and event filtering of Kafka ESM with the same identity governance you apply everywhere else. It works with self-hosted clusters and managed offerings including Confluent Cloud, Aiven, and Redpanda.

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:

• Introducing Strands Box: AI agent sandboxes powered by Dogwood – As more of us let agents run shell commands and call APIs without approving every step, isolation alone stops being enough. Strands Box is an open source sandbox (Apache 2.0, developer preview) that combines OS-level containment with fine-grained policies written in Dogwood, so permissions can depend on what the agent has already done. You can say “let the agent post to the incident channel, but no more than three times every 10 minutes” and have that enforced from outside the agent’s process. It also keeps real credentials out of the agent’s environment by attaching them at an egress gateway.

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

• AWS re:Invent – re:Invent returns to Las Vegas from November 30 to December 4, and reserved seating is now open. The session catalog is live, so register and claim your spots in chalk talks, workshops, and builders’ sessions before they fill up.

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!

The post AWS Weekly Roundup: Prime Day 2026 by the numbers, Claude Haiku 5.5, Amazon Nova 2.5 Sonic, and more (October 13, 2026) appeared first on The NOKIA Blog.

Amazon Prime Day 2026 was exclusively for Prime members and ran June 23–26, 2026 with millions of deals across more than 35 categories.

As part of our annual tradition of sharing how AWS powered Prime Day (2016, 2017, 2019, 2020, 2021, 2022, 2023, 2024, and 2025), I want to share the services and chart-topping metrics from AWS that made your amazing shopping experience possible.

Prime Day 2026 – all the numbers
As always, Prime Day was powered by AWS. Here are some of the most interesting and/or mind-blowing metrics:

Amazon Elastic Compute Cloud (Amazon EC2) and AWS Graviton – During Amazon Prime Day 2026, AWS Graviton powered up to 49% of the Amazon EC2 compute used by Amazon.com.

Amazon Elastic Block Store (Amazon EBS) – During Prime Day 2026, Amazon EBS, our high-performance block storage service, peaked at over 24.8 trillion I/O operations, moving over an exabyte of data daily.

AWS Lambda – AWS Lambda handled over 2.3 trillion invocations per day during Prime Day 2026.

Amazon Elastic Container Service (ECS) and Fargate – During Prime Day 2026, Amazon ECS launched an average of 158.3 million tasks per day on AWS Fargate, representing a 47.7 percent increase from the previous year’s Prime Day average.

Amazon CloudFront – Amazon CloudFront delivered over 2.1 trillion HTTP requests during the global week of Prime Day 2026, a 5 percent increase in total requests compared to Prime Day 2025.

Amazon DynamoDB – Amazon DynamoDB, a serverless, fully managed, distributed NoSQL database, powers multiple high-traffic Amazon properties and systems including Alexa, the Amazon.com sites, and all Amazon fulfillment centers. Over the course of Prime Day 2026, between June 23 and June 26, 2026, DynamoDB processed over 59 trillion requests. DynamoDB maintained high availability while delivering single-digit millisecond responses and peaking at 192 million requests per second.

Amazon Aurora – On Prime Day, Amazon Aurora, a relational database management system (RDBMS) built for high performance and availability at global scale for PostgreSQL, MySQL, and DSQL, processed hundreds of billions of transactions, stored 5,491 terabytes of data, and transferred 1,194 terabytes of data.

Amazon ElastiCache – During Prime Day, Amazon ElastiCache peaked at serving over 2.3 quadrillion daily requests and 2.1 trillion requests in a minute.

Amazon Kinesis Data Streams – Amazon Kinesis Data Streams, a fully managed serverless data streaming service, processed a peak of 988 million records per second during Prime Day 2026.

Amazon Simple Notification Service (SNS) – Amazon SNS, a fully managed pub/sub messaging service for application-to-application and application-to-person communication, delivered 5 trillion messages in a single day during Prime Day 2026.

Amazon Simple Queue Service (SQS) – Amazon SQS, a fully managed message queuing service for microservices, distributed systems, and serverless applications, received a peak of 213 million messages per second during Prime Day 2026

AWS CloudTrail – AWS CloudTrail processes billions of API activity events per day for governance, compliance, and operational auditing. During Prime Day 2026, that volume surged to 3.6 trillion events in just four days, a 44% increase over Prime Day 2025.

AWS CloudWatch – Amazon CloudWatch processed over 2.15 quadrillion metric observations per day during Prime Day 2026.

Amazon GuardDuty – During Prime Day 2026, Amazon GuardDuty monitored an average of 14.08 trillion log events per hour, a 59% increase from last year’s Prime Day.

AWS Fault Injection Service (FIS) – We ran over 44,000 AWS FIS experiments – more than six times what we conducted in 2025 – to help ensure Amazon.com remains highly available on Prime Day.

Prepare to scale
If you’re preparing for similar business-critical events, product launches, and migrations, I recommend that you take advantage of AWS Countdown Premium. From retail peak seasons to major sporting events, elections, and healthcare enrollment periods, we help you deliver flawless experiences when it matters most. Our experts help you manage your infrastructure to handle massive traffic spikes while maintaining security and performance. We work alongside your team to scale infrastructure, optimize costs during demand surges, strengthen security measures, and monitor real-time demands. To learn more, visit AWS Countdown Premium for business critical events.

I look forward to seeing what other records will be broken next year!

— Channy

The post All the numbers: Amazon Prime Day 2026 powered by AWS appeared first on The NOKIA Blog.

Amazon Web Services (AWS) started with a handful of foundational infrastructure services such as Amazon Simple Storage Service (Amazon S3), Amazon Elastic Compute Cloud (Amazon EC2), and Amazon Simple Queue Service (Amazon SQS), so that anyone with an idea could start building. As the world’s largest companies and governments adopted AWS, they asked for features to optimize their configuration for a range of global business contexts, security requirements, and operational needs. To meet these needs, AWS expanded globally through new Regions and added breadth and depth of services in security, networking, governance, and cost controls, so those customers could operate wherever they needed and at the scale they require. That combination of global reach, breadth, and depth remains essential for those customers, but if you are at the start of a new idea, every configuration option is effort standing in the way of shipping your dream product fast.

Today, we’re announcing a new simplified experience on AWS for builders who are working at the pace of AI. Instead of having to complete configuration tasks before you can work on your project, you start with sensible defaults and simple administration. You sign up using an existing identity from providers including Google, GitHub, and Apple. For most new customers, no credit card is required to start and you receive $100 in free credits as part of the AWS Free Tier. You can build immediately in your first project. As you continue to work, you can invite collaborators with just an email address, without learning about AWS Identity and Access Management (IAM) or AWS IAM Identity Center. When your project grows beyond the free credits, you can set a spend limit so you stay within your budget on the paid plan. If you grow to need additional customization, you can activate advanced AWS features to access the full breadth and depth of AWS without migrating.

How it works
When you sign up, AWS organizes your work in a project. A project contains an AWS account, where you create resources, and settings for sharing with team members. AWS creates that structure for you and applies additional security controls so you can start building your idea. After signing in, you get a prompt to paste into your coding agent that configures it to work with your new AWS environment. From there, your agent can deploy resources, run workloads, and iterate on your application following best practices for working with AWS.

You can create another project with a click. When you want to work with an additional team member, you send an invitation to their email address. Identity permissions are handled for you, so there are no IAM users to create; each person you invite only gets access to the projects you specify. Console workflows and coding agents also configure permissions between supported services and resources automatically, so you do not have to set up or troubleshoot resource permissions by hand.

When you’re ready to move beyond free credits, you can upgrade to a paid plan by entering your payment method. You can set a monthly spend limit on a project based on your usage trends, starting at $20 per month. The spend limit is the ceiling for that project’s costs, and you pay for what you actually use up to that amount. For example, if you set a $50 spend limit and your project incurs $32 in charges that month, you pay $32 (plus taxes). AWS will suggest a spend limit based on your usage, and you can accept that recommendation or set a custom amount if you are planning to further scale your usage. If your project approaches the limit, you first receive notifications. If spend reaches the limit, AWS pauses your project rather than accumulating charges, and you can resume working on it when you raise the limit. Each project has its own spend limit so you can give a larger budget to a workload that is gaining traction while keeping a smaller budget on an experimental idea.

Let’s try it out
To get started, I went to aws.amazon.com and chose Create account. I signed in with my Google account and within seconds had a new project ready to go, as shown in the following screenshot.

The Sign up for AWS page, with options to continue with email or sign in using Google, GitHub, Apple, or Amazon.

The first thing I saw was a prompt to configure my coding agent. I copied the prompt and pasted it into my agent. The agent set up the AWS Command Line Interface (AWS CLI) and the Agent Toolkit for AWS, logged me into AWS, and created a CLAUDE.md file in my project with guidance for the new experience.

The Setup Agent Toolkit for AWS dialog, with a prompt to copy and paste into your coding agent.

With the agent connected, I gave it a short prompt: build an API that returns a new unique sequential ID on every request. The agent created an AWS Lambda function, an Amazon DynamoDB table, and an Amazon API Gateway API, then deployed them for me. I did not have to configure resource permissions by hand. Within a few minutes I had a public endpoint that returned a newly minted ID on each request. My project started with $100 in free credits, and I received an additional $20 when the Lambda function was deployed.

A coding agent prompt to build an API that returns a unique sequential ID on every request.

The coding agent presents architecture options for the sequential ID API, with AWS Lambda and Amazon DynamoDB selected.

The coding agent confirms the API is live and lists the Amazon DynamoDB table, AWS Lambda function, and Amazon API Gateway API it deployed.

From the project, I could manage settings, invite team members by email, and monitor billing, as shown in the following screenshots.

The Projects page, showing remaining free-plan days, credits, and a project.

The project Members page, with the option to invite a new team member by email.

The Billing page, showing a $0.00 balance on the free plan, remaining credits, and cost by project.

Activating advanced features
If you reach the point where you need multiple Regions, or governance features like custom policies in AWS Organizations, you can activate advanced features at no additional cost. You’ll find yourself in a fully configured AWS Organization built according to best practices, with no migration and no downtime. Everything you configured previously is preserved and reflected in the underlying AWS services.

Now rolling out
We’ve heard from builders that they do not want to spend their first hours configuring an AWS environment. They want to build what they came to build, and we listened. AWS began as a place where anyone with an idea could start building, and this new simplified experience brings that starting point back, with sensible defaults so you can begin immediately, and with the global reach, breadth, and depth of AWS still there when your idea needs it. We are gradually rolling this experience out to new customers. We cannot wait to see what you build, and we want your feedback on the experience.

To try the new experience, create a new AWS account. To learn more, see Sign up for AWS (new).

The post AWS reimagines the getting started experience appeared first on The NOKIA Blog.

Last week, we brought together AWS Heroes from around the world to connect, collaborate, and celebrate the builders who go above and beyond for the AWS community.

The AWS Heroes Summit, an invite-only annual gathering, brings global experts specializing in fields like AI, serverless, and containers together for direct collaboration, technical deep-dives, and feedback sessions with internal AWS product and service teams.

Day 1 started with an inspiring fireside chat from AWS CEO Matt Garman. From an insightful AMA with James Hamilton on Day 2 to breakout sessions from various product teams that sparked new ideas, our AWS Heroes excelled at sharing knowledge, lifting each other up, and turning conversations into collaborations. To learn more, read the attendee feedback on LinkedIn.

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

• Web Search on Amazon Bedrock: Amazon Bedrock now enables OpenAI models (GPT-5.4, GPT-5.5, and GPT-5.6 Sol/Terra/Luna) to browse and retrieve information from the internet, allowing AI applications to access up-to-date information beyond their training data. This capability opens new possibilities for building AI agents and applications that can answer questions using real-time web content while maintaining data residency within your secured AWS environment with zero data egress. To get started, visit the AI blog post and the Amazon Bedrock User Guide.
• Runtime Instances on Amazon Bedrock AgentCore: You can now deploy and run AI agents on dedicated runtime instances through Amazon Bedrock AgentCore, providing more control over agent execution environments with predictable performance and cost. To get started, visit Sébastien’s blog post and AgentCore documentation.
• Vector search for Amazon DynamoDB: You can store and query vector embeddings alongside your existing data in DynamoDB without managing a separate vector database. DynamoDB already supports storing memory for AI agents, and with vector search you can now add semantic retrieval over that memory for agentic grounding, with predictable performance. To learn more, visit Esra’s blog post and Amazon DynamoDB Developer Guide.
• AWS Transform continuous modernization now generally available: This capability helps engineering teams analyze and remediate technical debt across source code repositories at scale. You can modernize mainframe and legacy workloads with an ongoing, automated approach rather than a one-time migration event. To learn more, visit Micah’s preview blog post. You can also try the AWS Transform Kiro Power and agent plugins.
• Up to 3,000 Mbps for AWS Lambda function bandwidth: AWS Lambda functions now support increased network bandwidth, enabling data-intensive workloads and faster communication between Lambda functions and other AWS services. This feature enables functions outside a VPC that are configured with 2 GB of memory or more to access network bandwidth that scales proportionally, from 625 Mbps at 2 GB up to 3,000 Mbps at 10 GB.

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 that you may find interesting:

• Introducing Dogwood: Runtime Verification for AI Agents: AWS open-sourced Dogwood, a purpose-built governance language for AI agents to support Cedar policies and add temporal conditions. Powering Dogwood, Amazon Bedrock AgentCore introduced temporal policies whose decisions depend on the history of an agent’s actions within a session, not on the current request alone.
• AWS supports Agent Plugins: An Open Standard for Portable Agent Extensions: AWS announced support for Agent Plugins, an open source, vendor-neutral specification that gives AI agent extensions a common packaging format so you can package an extension once and ship it to any client, including Kiro, VS Code, Cursor, or any tool that implements the spec.
• Introducing Kiro Crew: Kiro Crew is a persistent, self-evolving workspace that keeps work moving, online or off, enabling collaborative multi-agent development workflows within the Kiro IDE. It’s built for engineering work that goes beyond a single chat session, and spans repos, tools, and days. You can run several efforts in parallel or hand work to subagents that report back, so nothing waits in line.

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

The post AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026) appeared first on The NOKIA Blog.

Today, we’re announcing the general availability of vector search in Amazon DynamoDB. You can now store vector embeddings alongside your operational data in DynamoDB and run similarity searches directly against that data, without replicating it to a separate vector store.

DynamoDB supports native vector search with single-digit millisecond latency at 99%+ recall, and is designed for any scale, even trillions of vectors. There are no servers to provision, patch, or manage, and no software to install, maintain, or operate. The service has no versions, no maintenance windows, and zero downtime maintenance.

Vector indexes have no storage limits and scale horizontally as your data grows. You can now build applications that require semantic retrieval on agentic memory, retrieval augmented generation, recommendation engines, personalized experiences, anomaly detection, and more using DynamoDB and its native vector search.

If your application already uses DynamoDB, adding vector search previously required copying data into a dedicated vector database while maintaining a synchronization pipeline between the two services. This added operational overhead, data movement costs, licensing costs, and the challenge of maintaining predictable low latency at scale. With vector search built into DynamoDB, your vectors and operational data share the same serverless infrastructure and the same pay-per-request pricing model.

Vector search in DynamoDB introduces a new index type that you create on an attribute storing vector embeddings. You generate embeddings using a model of your choice, such as Amazon Bedrock Titan Text Embeddings, Cohere Embed, or OpenAI text embedding models, and store them as a list of floats in your table using a standard PutItem call. You then create a vector index on that attribute and specify the number of dimensions, the distance function, and any non-vector attributes you want to use as filters to narrow search results at query time. The SearchVectors API accepts a query vector, the number of results to return (up to 100), and optional filter conditions. It returns results ranked by similarity.

Use vector search in DynamoDB when your operational data already lives in DynamoDB and you want to add similarity search without provisioning a separate database or managing a synchronization pipeline. DynamoDB is fully serverless, so vector search scales automatically with no infrastructure to manage. It supports up to 4096 dimensions, Euclidean, Cosine, and Dot product distance functions, and inline filtering.

Getting started with vector search in DynamoDB
This walkthrough shows how to add vector search to an existing DynamoDB table using the DynamoDB console. The scenario contains an online sporting goods store with a product catalog table. Each item has standard operational attributes such as productId, category, description, marketplace, name, and price. The goal is to add semantic search so shoppers can find products using natural language queries rather than exact keyword matches.

1. Prepare DynamoDB table
To enable semantic search, I first generate vector embeddings for the product descriptions already in my table. Embeddings are numerical representations of text generated by a machine learning model that capture the meaning of the content. Two items with similar descriptions will have embeddings that are close to each other in vector space, which is what makes similarity search possible.

I can generate embeddings using Amazon Bedrock Titan Text Embeddings or another embedding model, then add them to my table using the AWS Management Console, AWS Command Line Interface (AWS CLI), AWS SDKs, AWS CloudFormation, or other infrastructure-as-code (IaC) tools.

For an existing table like ProductCatalog, I add the embeddings to each item as a new attribute named descriptionEmbedding using an UpdateItem call. DynamoDB stores vector embeddings using its existing List data type. Each element in the list is a Number that represents a single float value of the embedding vector. This means I do not need a new data type or schema change to start storing vectors alongside my existing operational attributes.

2. Create vector index
In the DynamoDB console, open the ProductCatalog table and choose the Indexes tab. I choose Create vector index. On the Create vector index page, I fill in the index details as follows. I enter ProductDescriptionIndex as the Index name and descriptionEmbedding as the Vector attribute.

I enter the number of Dimensions that matches my embedding model’s output and select Cosine as the Distance function. Cosine measures the angle between vectors rather than their magnitude, which makes it effective for comparing semantic similarity of text embeddings. Vector search in DynamoDB also supports Euclidean and Dot product distance functions.

• Euclidean: Use when the magnitude of the vectors is meaningful, such as clustering items by a numeric value like purchase count.
• Dot product: Use when both direction and magnitude matter, such as in recommendation systems that weight interest alignment and frequency together. As a general rule, match the distance function to the one used to train your embedding model for the best accuracy.

I enter marketplace as the Partition key. The vector index partition key controls how DynamoDB distributes vectors across partitions, allowing the index to scale out while maintaining predictable latencies. Each search is scoped to a single partition key value, so a product catalog serving multiple marketplaces can search within one marketplace’s inventory without scanning the entire index. The partition key is optional, but recommended for large datasets with high query throughput.

I expand Inline filter attributes and add category as a filter attribute. This helps me narrow search results to a specific product category at query time. Filter conditions support exact-match values only; range conditions such as BETWEEN or BEGINS_WITH are not supported. I leave Attribute projections set to All so that all table attributes are returned with my search results. Choose Create vector index and wait for the index status to change to Active.

3. Run vector search
I generate a query vector from a natural language search term such as “lightweight running shoes for summer” using the same embedding model I used for the product descriptions. In the DynamoDB console, I choose Explore items in the left navigation pane and select the ProductCatalog table.

Choose Search to switch to vector search mode. I select ProductDescriptionIndex from the Select a vector index dropdown, paste the query vector into the Search vector field, and set Number of results (Top K) to 5. I enter US as the Partition key value to scope the search to the US marketplace. I expand Inline filter attributes and set category equal to footwear to narrow the search to footwear products only. Now, choose Run.

DynamoDB returns the five most semantically similar products in the footwear category, ranked by similarity score, alongside the standard operational attributes such as name and price in the same response. The similarity score’s meaning depends on the distance function selected for the index. For Cosine and Euclidean distance functions, lower similarity score values indicate higher similarity, with a score of 0 indicating identical vectors. For the dot product distance function, higher similarity score values indicate higher similarity.

To interact with vector search programmatically, including calling APIs and searching documentation, try the AWS MCP Server and plugins with your preferred AI coding tool. To learn more, visit the Amazon DynamoDB Developer Guide.

Get started today
Vector search in Amazon DynamoDB is generally available in all commercial AWS Regions, including the AWS GovCloud (US) Regions. For Regional availability and a future roadmap, visit the AWS Capabilities by Region. For pricing details, visit the Amazon DynamoDB pricing page.

Start exploring vector search in DynamoDB today and send feedback to AWS re:Post for Amazon DynamoDB or through your usual AWS Support contacts.

— Esra

The post Amazon DynamoDB now supports real-time vector search at any scale appeared first on The NOKIA Blog.

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

The post AWS Weekly Roundup: One-click Lambda setup prompt, OpenAI GPT-5.6 models on Bedrock, and more (July 20, 2026) appeared first on The NOKIA Blog.

There’s something genuinely energizing about working with startups — something I’ve been doing intensely for more than two years now. Startups operate at a different frequency: the urgency is real, the constraints are tight, and the stakes are personal. Helping them navigate the challenge of proving their business model requires not just technical depth but a willingness to move fast, challenge assumptions, and make bets on the right architecture before the perfect data exists.

What I love most is that the work is never abstract: every decision I help a startup make has a direct impact on whether they ship on time, stay within budget, and earn the next round of confidence from their investors.

Let’s dive into this week’s AWS news.

Headlines

Now Open — AWS Local Zones in Istanbul, Türkiye — AWS has opened a new Local Zone in Istanbul, Türkiye, bringing AWS compute, storage, and networking services to one of Europe’s largest metropolitan areas. AWS Local Zones place AWS infrastructure much closer to large population and industry, enabling organizations to store and process data within specific jurisdictions to meet data residency requirements, while delivering single-digit millisecond latency for applications that need to run closer to end users. This supports compliance needs across financial services, government, telecoms, and healthcare.

A Local Zone is a significant infrastructure investment: it requires the same level of operational excellence as a Region, with consistent hardware, power, and networking infrastructure, and reflects AWS’s continued expansion into underserved markets.

For builders in Türkiye, this opens up a new set of architectural possibilities. You can now store and back up data within Turkish borders to help meet data residency requirements, and run latency-sensitive workloads in the Istanbul Local Zone while connecting seamlessly to the AWS Region—giving you the flexibility to architect hybrid applications without managing your own data center infrastructure.

To learn more about our decade-long commitment, available services, customers and partners in Türkiye, visit the launch blog post.

Last week’s launches

Here are some launches and updates that caught my attention:

• Security Hub Extended expands to 21 curated partner solutions across 9 categories — AWS Security Hub Extended now integrates with 21 curated partner security solutions spanning 9 categories, including endpoint protection, cloud security posture management, threat intelligence, and more. You can now get consolidated, prioritized security findings from a broader ecosystem of tools directly within Security Hub, without requiring custom integrations. This is particularly valuable for enterprise security teams that want a unified view of their security posture across AWS and third-party tooling.
• Amazon SageMaker AI now supports OpenAI-compatible APIs for inference endpoints — You can now call Amazon SageMaker AI inference endpoints using OpenAI-compatible APIs, making it significantly easier to migrate AI workloads from OpenAI to SageMaker — or to build applications that work across multiple providers — with no SDK changes required. This lowers the migration barrier for teams that started prototyping with OpenAI and are now looking to move to a more scalable, cost-controlled infrastructure on AWS. Your existing application code works as-is; you simply point it at your SageMaker endpoint.
• Introducing pre-fetching and IAM role assumption for AWS Secrets Manager Agent — The AWS Secrets Manager Agent can now pre-fetch secrets at startup and assume IAM roles to retrieve them, eliminating the cold-start latency associated with on-demand secret retrieval in latency-sensitive applications. You can configure the agent to preload the secrets your application needs before it starts serving traffic, reducing the risk of secrets-related latency spikes in production. IAM role assumption support also makes it easier to share the agent across workloads with different permission boundaries.
• AWS announces ExtendDB, an open-source DynamoDB-compatible adapter — AWS has open-sourced ExtendDB, a DynamoDB-compatible adapter that allows you to use the DynamoDB API and data model on top of alternative backend storage systems. This is particularly useful for local development and testing workflows — you can write against the DynamoDB API without requiring a live AWS connection. It’s also valuable for scenarios where you need DynamoDB-compatible semantics with more control over the underlying storage layer. It’s a practical tool for teams that want to build portability into their data access layer.
• AWS SAM CLI adds AWS CloudFormation Language Extensions support to accelerate local serverless development — The AWS SAM CLI now supports AWS CloudFormation Language Extensions locally, meaning you can use transforms, dynamic references, and other CloudFormation language features directly in your local development and testing workflows. This closes a long-standing gap between what you can test locally and what runs in production, making local serverless development faster and more reliable. If you build serverless applications with SAM and encounter edge cases in local testing, this update will meaningfully improve your experience.

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:

• Amazon Bedrock introduces new advanced prompt optimization and migration tool — This post covers the newly launched Advanced Prompt Optimization and Migration Tool in Amazon Bedrock, which helps you automatically tune your prompts for better model performance and assists you in migrating prompts across different foundation models. It’s a must-read if you’re iterating on prompt quality for production AI workloads.
• Introducing Kiro Web — Kiro, AWS’s AI-powered development environment, now has a web-based interface. Kiro Web lets you access Kiro’s spec-driven development, AI chat, and agent capabilities directly from your browser, without needing to install the desktop IDE. This is a great step toward making AI-assisted development more accessible — whether you’re doing a quick review, prototyping from a new machine, or introducing your team to the Kiro workflow.
• Announcing updated retry behavior for AWS SDKs and Tools — AWS has updated the default retry behavior across its SDKs and CLI tools, improving resilience for transient errors without requiring configuration changes from developers. The updated behavior includes smarter backoff strategies and better handling of throttling responses. If you’re running production workloads that occasionally hit API rate limits or transient failures, this update improves reliability out of the box. It’s worth reading to understand what changed and how it affects your applications.
• Bitnami image removal from ECR Public — AWS has announced that Bitnami container images will be removed from Amazon ECR Public. If your workloads pull Bitnami images from ECR Public, you should review this post to understand the timeline and migration path. The Bitnami images remain available directly from Bitnami’s own registry, and this post explains how to update your image references to continue pulling them without interruption.

Upcoming AWS events

Check your calendar and sign up for these events:

• AWS Summit Amsterdam — Join us in Amsterdam on May 27 for a full day of cloud and AI sessions, hands-on labs, and networking with builders and AWS experts from across Europe. Registration is free.
• AWS Summit Bangkok — AWS Summit Bangkok takes place on May 28. It’s a fantastic opportunity for builders and customers across Southeast Asia to connect and explore the latest in cloud innovation.
• AWS Summit Milan — Also on May 28, AWS Summit Milan brings the AWS community together in Italy. If you’re in Southern Europe, this is your event.
• AWS Summit Mumbai — Also on May 28, AWS Summit Mumbai brings cloud and AI content to builders across India. Check the link for the full agenda and registration.
• AWS Summit Los Angeles — Mark your calendar for June 10 in Los Angeles. The AWS Summit LA is coming up and it’s a great opportunity to connect with the West Coast builder community.
• 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!

The post AWS Weekly Roundup: AWS Local Zones in Istanbul, open-source ExtendDB, Kiro Web, and more (May 25, 2026) appeared first on The NOKIA Blog.

At re:Invent 2025, Colin Lazier, vice president of databases at AWS, emphasized the importance of building at the speed of an idea—enabling rapid progress from concept to running application. Customers can already create production-ready Amazon DynamoDB tables and Amazon Aurora DSQL databases in seconds. He previewed creating an Amazon Aurora serverless database with the same speed, and customers have since requested quick access and speed to this capability.

Today, we’re announcing the general availability of a new express configuration for Amazon Aurora PostgreSQL, a streamlined database creation experience with preconfigured defaults designed to help you get started in seconds.

With only two clicks, you can have an Aurora PostgreSQL serverless database ready to use in seconds. You have the flexibility to modify certain settings during and after database creation in the new configuration. For example, you can change the capacity range for the serverless instance at the time of create or add read replicas, modify parameter groups after the database is created. Aurora clusters with express configuration are created without an Amazon Virtual Private Cloud (Amazon VPC) network and include an internet access gateway for secure connections from your favorite development tools – no VPN, or AWS Direct Connect required. Express configuration also sets up AWS Identity and Access Management (IAM) authentication for your administrator user by default, enabling passwordless database authentication from the beginning without additional configuration.

After it’s created, you have access to features available for Aurora PostgreSQL serverless, such as deploying additional read replicas for high availability and automated failover capabilities. This launch also introduces a new internet access gateway routing layer for Aurora. Your new serverless instance comes enabled by default with this feature, which allows your applications to connect securely from anywhere in the world through the internet using the PostgreSQL wire protocol from a wide range of developer tools. This gateway is distributed across multiple Availability Zones, offering the same level of high availability as your Aurora cluster.

Creating and connecting to Aurora in seconds means fundamentally rethinking how you get started. We launched multiple capabilities that work together to help you onboard and run your application with Aurora. Aurora is now available on AWS Free Tier, which you gain hands-on experience with Aurora at no upfront cost. After it’s created, you can directly query an Aurora database in AWS CloudShell or using programming languages and developer tools through a new internet accessible routing component for Aurora. With integrations such as v0 by Vercel, you can use natural language to start building your application with the features and benefits of Aurora.

Create an Aurora PostgreSQL serverless database in seconds

To get started, go to the Aurora and RDS console and in the navigation pane, choose Dashboard. Then, choose Create with a rocket icon.

Review pre-configured settings in the Create with express configuration dialog box. You can modify the DB cluster identifier or the capacity range as needed. Choose Create database.

You can also use the AWS Command Line Interface (AWS CLI) or AWS SDKs with the parameter --with-express-configuration to create both a cluster and an instance within the cluster with a single API call which makes it ready for running queries in seconds.To learn more, visit Creating an Aurora PostgreSQL DB cluster with express configuration.

Here is a CLI command to create the cluster:

$ aws rds create-db-cluster --db-cluster-identifier channy-express-db 
    --engine aurora-postgresql 
    –-with-express-configuration

Your Aurora PostgreSQL serverless database should be ready in seconds. A success banner confirms the creation, and the database status changes to Available.

After your database is ready, go to the Connectivity & security tab to access three connection options. When connecting through SDKs, APIs, or third-party tools including agents, choose Code snippets. You can choose various programming languages such as .NET, Golang, JDBC, Node.js, PHP, PSQL, Python, and TypeScript. You can paste the code from each step into your tool and run the commands.

For example, the following Python code is dynamically generated to reflect the authentication configuration:

import psycopg2
import boto3

auth_token = boto3.client('rds', region_name='ap-south-1').generate_db_auth_token(DBHostname='channy-express-db-instance-1.abcdef.ap-south-1.rds.amazonaws.com', Port=5432, DBUsername='postgres', Region='ap-south-1')

conn = None
try:
    conn = psycopg2.connect(
        host='channy-express-db-instance-1.abcdef.ap-south-1.rds.amazonaws.com',
        port=5432,
        database='postgres',
        user='postgres',
        password=auth_token,
        sslmode='require'
    )
    cur = conn.cursor()
    cur.execute('SELECT version();')
    print(cur.fetchone()[0])
    cur.close()
except Exception as e:
    print(f"Database error: {e}")
    raise
finally:
    if conn:
        conn.close()

Choose CloudShell for quick access to the AWS CLI which launches directly from the console. When you choose Launch CloudShell, you can see the command is pre-populated with relevant information to connect to your specific cluster. After connecting to the shell, you should see the psql login and the postgres => prompt to run SQL commands.

You can also choose Endpoints to use tools that only support username and password credentials, such as pgAdmin. When you choose Get token, you use an AWS Identity and Access Management (IAM) authentication token generated by the utility in the password field. The token is generated for the master username that you set up at the time of creating the database. The token is valid for 15 minutes at a time. If the tool you’re using terminates the connection, you will need to generate the token again.

Building your application faster with Aurora databases

At re:Invent 2025, we announced enhancements to the AWS Free Tier program, offering up to $200 in AWS credits that can be used across AWS services. You’ll receive $100 in AWS credits upon sign-up and can earn an additional $100 in credits by using services such as Amazon Relational Database Service (Amazon RDS), AWS Lambda, and Amazon Bedrock. In addition, Amazon Aurora is now available across a broad set of eligible Free Tier database services.

Developers are embracing platforms such as Vercel, where natural language is all it takes to build production-ready applications. We announced integrations with Vercel Marketplace to create and connect to an AWS database directly from Vercel in seconds and v0 by Vercel, an AI-powered tool that transforms your ideas into production-ready, full-stack web applications in minutes. It includes Aurora PostgreSQL, Aurora DSQL, and DynamoDB databases. You can also connect your existing databases created through express configuration with Vercel. To learn more, visit AWS for Vercel.

Like Vercel, we’re bringing our databases seamlessly into their experiences and are integrating directly with widely adopted frameworks, AI assistant coding tools, environments, and developer tools, all to unlock your ability to build at the speed of an idea.

We introduced Aurora PostgreSQL integration with Kiro powers, which developers can use to build Aurora PostgreSQL backed applications faster with AI agent-assisted development through Kiro. You can use Kiro power for Aurora PostgreSQL within Kiro IDE and from the Kiro powers webpage for one-click installation. To learn more about this Kiro Power, read Introducing Amazon Aurora powers for Kiro and Amazon Aurora Postgres MCP Server.

Now available

You can create an Aurora PostgreSQL serverless database in seconds today in all AWS commercial Regions. For Regional availability and a future roadmap, visit the AWS Capabilities by Region.

You pay only for capacity consumed based on Aurora Capacity Units (ACUs) billed per second from zero capacity, which automatically starts up, shuts down, and scales capacity up or down based on your application’s needs. To learn more, visit the Amazon Aurora Pricing page.

Give it a try in the Aurora and RDS console and send feedback to AWS re:Post for Aurora PostgreSQL or through your usual AWS Support contacts.

— Channy

The post Announcing Amazon Aurora PostgreSQL serverless database creation in seconds appeared first on The NOKIA Blog.

AWS has reached its 20th anniversary! With a steady pace of innovation, AWS has grown to offer over 240 comprehensive cloud services and continues to launch thousands of new features annually for millions of customers. During this time, over 4,700 posts have been published on this blog—more than double the number since Jeff Barr wrote the 10th anniversary post.

AWS changed my life

Reflecting on what I was doing 20 years ago, I met Jeff in Seoul on March 13, 2006, when he came as the keynote speaker for the Korea NGWeb conference. At that time, Amazon was one of the first pioneers to initiate an API economy, introducing ecommerce API services. After the keynote speech, he returned home that evening, and I believe he wrote the Amazon S3 launch blog post on the flight back to the United States.

That short meeting with him brought significant changes to my life. He became my role model as a blogger, and I began building API-based services in my company and opening them to third-party developers. When I was a PhD student while taking a break from work, I realized that for individual researchers like me, AWS Cloud services are powerful tools for conducting large-scale research projects. After returning to work, my company became one of the first AWS customers in Korea in 2014. Countless developers—myself included—have embraced cloud computing and actively used its capabilities to accomplish what was previously impossible.

Over the past decade, the technology landscape has transformed dramatically. Deep learning emerged as a breakthrough in AI, evolving through generative AI based on large language models (LLMs) to today’s agentic AI technology. Jeff wrote, “When looking into the future, you need to be able to distinguish between flashy distractions and genuine trends, while remaining flexible enough to pivot if yesterday’s niche becomes today’s mainstream technology.” This principle guides how AWS approaches innovation—we start by listening to what customers truly need. The real trend isn’t pursuing every emerging technology, but rather reimagining solutions that address customers’ most critical challenges.

20 years of AWS

For the first 10 years, Jeff selected his favorite AWS launches and blog posts. Amazon S3, Amazon EC2 (2006), Amazon Relational Database Service, Amazon Virtual Private Cloud (2009), Amazon DynamoDB, Amazon Redshift (2012), Amazon WorkSpaces, Amazon Kinesis (2013), AWS Lambda (2014), and AWS IoT (2015).

While I also hate to play favorites, I want to choose some of my favorite AWS blog posts of the past decade.

• Deploying containers easily (2014) – Amazon Elastic Container Service makes it straightforward for you to run any number of containers across a managed cluster of Amazon EC2 instances using powerful APIs and other tools. In 2017, we launched Amazon Elastic Kubernetes Service as a fully managed Kubernetes service and AWS Fargate as a serverless deployment option.
• High availability database at global scale (2017) – Amazon Aurora is a modern relational database service offering performance and high availability at scale. In 2018, we launched Amazon Aurora Serverless v1, and this serverless database evolved to Amazon Aurora Serverless v2 to scale down to zero. In 2025, we also launched Amazon Aurora DSQL is the fastest serverless distributed SQL database for always available applications.
• Machine learning (ML) at your fingertips (2017) – Amazon SageMaker is a fully managed end-to-end ML service that data scientists, developers, and ML experts can use to quickly build, train, and host machine learning models at scale. In 2024, we launched the next generation of Amazon SageMaker, a unified platform for data, analytics, and AI and introduced Amazon SageMaker AI to focus specifically on building, training, and deploying AI and ML models at scale.
• Best price performance for cloud workloads (2018) – We launched Amazon EC2 A1 instances powered by the first generation of Arm-based AWS Graviton Processors designed to deliver the best price performance for your cloud workloads. Last year, we previewed EC2 M9g instances powered by AWS Graviton5 processors. Over 90,000 AWS customers have reaped the benefits of Graviton supporting popular AWS services such as Amazon ECS and Amazon EKS, AWS Lambda, Amazon RDS, Amazon ElastiCache, Amazon EMR, and Amazon OpenSearch Service.
• Run AWS Cloud in your data center (2019) – AWS Outposts is a family of fully managed services delivering AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience. Now, AWS Outposts is available in a variety of form factors, from 1U and 2U Outposts servers to 42U Outposts racks, and multiple rack deployments. Customers such as DISH, Fanduel, Morningstar, Philips, and others use Outposts in workloads requiring low latency access to on-premises systems, local data processing, data residency, and application migration with local system interdependencies.
• Best price performance for ML workloads (2019) – We launched Amazon EC2 Inf1 instances powered by the first generation of AWS Inferentia chips designed to provide fast, low-latency inferencing. In 2022, we launched Amazon EC2 Trn1 instances powered by the first generation of AWS Trainium chips optimized for high performance AI training. Last year, we launched Amazon EC2 Trn3 UltraServers powered by Trainium3 to deliver the best token economics for next-generation generative AI applications. Customers such as Anthropic, Decart, poolside, Databricks, Ricoh, Karakuri, SplashMusic, and others are realizing performance and cost benefits of Trainium-based instances and UltraServers.
• Build your generative AI apps on AWS (2023) – Amazon Bedrock is a fully managed service that offers a choice of industry leading AI models along with a broad set of capabilities that you need to build generative AI applications, simplifying development with security, privacy, and responsible AI. Last year, we introduced Amazon Bedrock AgentCore, an agentic platform for building, deploying, and operating effective agents securely at scale. Now, more than 100,000 customers worldwide choose Amazon Bedrock to deliver personalized experiences, automate complex workflows, and uncover actionable insights.
• Your AI coding companion (2023) – We launched Amazon CodeWhisperer as the industry’s first cloud-based AI coding assistant service. The service delivered code generation from comments, open-source code reference tracking, and vulnerability scanning capabilities. In 2024, we rebranded the service to Amazon Q Developer and expanded its features to include a chat-based assistant in the console, project-based code generation, and code transformation tools. In 2025, this service evolved into Kiro, a new agentic AI development tool that brings structure to AI coding through spec-driven development, taking projects from prototype to production. Recently, Kiro previewed an autonomous agent, a frontier agent that works independently on development tasks, maintaining context and learning from every interaction.
• Broaden your AI model choices (2024) – We launched Amazon Titan models further increasing cost-effective AI model choice for text and multimodal needs in Amazon Bedrock. At AWS re:Invent 2024, we announced Amazon Nova models that delivers frontier intelligence and industry leading price performance. Now Amazon Nova has a portfolio of AI offerings—including Amazon Nova models, Amazon Nova Forge, a new service to build your own frontier models; and Amazon Nova Act, a new service to build agents that automate browser-based UI workflows powered by a custom Amazon Nova 2 Lite model.

Build with AI: Your path forward

A decade ago, AWS responded to the emergence of deep learning by launching the broadest and deepest ML services, such as Amazon SageMaker, democratizing AI for a wide range of customers—from individual developers and startups to large enterprises—regardless of their technical expertise.

AI technology has advanced significantly, but building and deploying AI models and applications still remains complex for many developers and organizations. AWS offers the broadest selection of AI models through Amazon Bedrock, including leading providers such as Anthropic and OpenAI. By using our model training and inference infrastructure and responsible AI both practical and scalable, you can accelerate trusted AI innovation while maintaining control of your data and costs—all built on our global infrastructure’s operational excellence.

Reinvent your idea, keep on learning, build confidently with AI you can trust, and share your successes with us! New AWS customers receive up to $200 in credits to try AWS AI for free. If you’re a student, start building with Kiro for free using 1,000 credits per month for one year.

— Channy

The post 20 years in the AWS Cloud – how time flies! appeared first on The NOKIA Blog.

Here are the notable launches and updates from last week that can help you build, scale, and innovate on AWS.

Last week’s launches

Here are the launches that got my attention this week.

Let’s start with news related to compute and networking infrastructure:

• Introducing Amazon EC2 C8id, M8id, and R8id instances: These new Amazon EC2 C8id, M8id, and R8id instances are powered by custom Intel Xeon 6 processors. These instances offer up to 43% higher performance and 3.3x more memory bandwidth compared to previous generation instances.
• AWS Network Firewall announces new price reductions: The service has added the hourly and data processing discounts on NAT Gateways that are service-chained with Network Firewall secondary endpoints. Additionally, AWS Network Firewall has removed additional data processing charges for Advanced Inspection, which enables Transport Layer Security (TLS) inspection of encrypted network traffic.
• Amazon ECS adds Network Load Balancer support for Linear and Canary deployments: Applications that commonly use NLB, such as those requiring TCP/UDP-based connections, low latency, long-lived connections, or static IP addresses, can take advantage of managed, incremental traffic shifting natively from ECS when rolling out updates.
• AWS Config now supports 30 new resource types: These range across key services including Amazon EKS, Amazon Q, and AWS IoT. This expansion provides greater coverage over your AWS environment, enabling you to more effectively discover, assess, audit, and remediate an even broader range of resources.
• Amazon DynamoDB global tables now support replication across multiple AWS accounts: DynamoDB global tables are a fully managed, serverless, multi-Region, and multi-active database. With this new capability, you can replicate tables across AWS accounts and Regions to improve resiliency, isolate workloads at the account level, and apply distinct security and governance controls.
• Amazon RDS now provides an enhanced console experience to connect to a database: The new console experience provides ready-made code snippets for Java, Python, Node.js, and other programming languages as well as tools like the psql command line utility. These code snippets are automatically adjusted based on your database’s authentication settings. For example, if your cluster uses IAM authentication, the generated code snippets will use token-based authentication to connect to the database. The console experience also includes integrated CloudShell access, offering the ability to connect to your databases directly from within the RDS console.

Then, I noticed three news items related to security and how you authenticate on AWS:

• AWS Builder ID now supports Sign in with Apple: AWS Builder ID, your profile for accessing AWS applications including AWS Builder Center, AWS Training and Certification, AWS re:Post, AWS Startups, and Kiro, now supports sign-in with Apple as a social login provider. This expansion of sign-in options builds on the existing sign-in with Google capability, providing Apple users with a streamlined way to access AWS resources without managing separate credentials on AWS.
• AWS STS now supports validation of select identity provider specific claims from Google, GitHub, CircleCI and OCI: You can reference these custom claims as condition keys in IAM role trust policies and resource control policies, expanding your ability to implement fine-grained access control for federated identities and help you establish your data perimeters. This enhancement builds upon IAM’s existing OIDC federation capabilities, which allow you to grant temporary AWS credentials to users authenticated through external OIDC-compatible identity providers.
• AWS Management Console now displays Account Name on the Navigation bar for easier account identification: You now have an easy way to identify your accounts at a glance. You can now quickly distinguish between accounts visually using the account name that appears in the navigation bar for all authorized users in that account.
• Amazon CloudFront announces mutual TLS support for origins: Now with origin mTLS support, you can implement a standardized, certificate-based authentication approach that eliminates operational burden. This enables organizations to enforce strict authentication for their proprietary content, ensuring that only verified CloudFront distributions can establish connections to backend infrastructure ranging from AWS origins and on-premises servers to third-party cloud providers and external CDNs.

Finally, there is not a single week without news around AI :

• Claude Opus 4.6 now available in Amazon Bedrock: Opus 4.6 is Anthropic’s most intelligent model to date and a premier model for coding, enterprise agents, and professional work. Claude Opus 4.6 brings advanced capabilities to Amazon Bedrock customers, including industry-leading performance for agentic tasks, complex coding projects, and enterprise-grade workflows that require deep reasoning and reliability.
• Structured outputs now available in Amazon Bedrock: Amazon Bedrock now supports structured outputs, a capability that provides consistent, machine-readable responses from foundation models that adhere to your defined JSON schemas. Instead of prompting for valid JSON and adding extra checks in your application, you can specify the format you want and receive responses that match it—making production workflows more predictable and resilient.

Upcoming AWS events

Check your calendars so that you can sign up for this upcoming event:

AWS Community Day Romania (April 23–24, 2026): This community-led AWS event brings together developers, architects, entrepreneurs, and students for more than 10 professional sessions delivered by AWS Heroes, Solutions Architects, and industry experts. Attendees can expect expert-led technical talks, insights from speakers with global conference experience, and opportunities to connect during dedicated networking breaks, all hosted at a premium venue designed to support collaboration and community engagement.

If you’re looking for more ways to stay connected beyond this event, join the AWS Builder Center to learn, build, and connect with builders in the AWS community.

Check back next Monday for another Weekly Roundup.

— seb

The post AWS Weekly Roundup: Claude Opus 4.6 in Amazon Bedrock, AWS Builder ID Sign in with Apple, and more (February 9, 2026) appeared first on The NOKIA Blog.