10/12/2026, 12:00:00 AM ~ 10/13/2026, 12:00:00 AM (UTC)

Recent Announcements

Amazon EC2 G7 instances now available in Europe (Frankfurt) and Europe (Stockholm) Regions

Amazon Elastic Compute Cloud (Amazon EC2) G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs are now available in Europe (Frankfurt) and Europe (Stockholm) Regions. G7 instances deliver up to 4.6x AI inference performance and up to 2.1 graphics performance compared to G6 instances. G7 instances also deliver faster performance for GPU-accelerated data analytics workloads.\nCustomers can use G7 instances for deploying AI models for language translation, video and image analysis, speech recognition and recommender systems. They also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics and game streaming. Additionally, G7 instances support video transcoding, spatial computing, and data analytics workloads. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU and custom Intel Xeon 6 processors. They support up to 192 virtual CPUs (vCPUs) and up to 700 Gbps of Elastic Fabric Adapter (EFA) networking bandwidth. They also support up to 768 GiB of system memory, and up to 7.6 TB of local NVMe SSD storage. You can use G7 instances for Amazon EC2 in the following AWS Regions: US West (Oregon), US East (N. Virginia, Ohio) and Europe (Spain, Frankfurt, Stockholm). You can purchase G7 instances as On-Demand Instances, Spot Instances, or as part of Savings Plans. To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit the G7 instance page.

AWS Lambda announces Lambda Web Functions

AWS Lambda announces Lambda Web Functions, a new serverless compute primitive where your code handles HTTP requests directly. Your code listens on a port and handles requests as it would on any HTTP server. You use frameworks you already know, such as Express or Hono, with no adapters or custom configuration. This enables you to serve workloads such as AI inference endpoints, server-side rendered (SSR) web apps, streaming API endpoints, microservices, and webhooks. You pay for active CPU time and the memory your environment holds, not for idle CPU time your code spends waiting on databases or downstream services. With this launch, we are also renaming existing Lambda functions to Lambda Event Functions, a naming change only that requires no code changes or redeployments for your existing functions.\nBuilders use Lambda to go from code to production quickly and without managing servers, across event-driven pipelines, request-response APIs, and data processing. As AI-driven development accelerates, they want to build locally and deploy with a familiar programming model and pay for workloads based on active CPU usage, while serving users with low latency. Lambda Web Functions delivers this. Today, Lambda Web Functions supports the Node.js runtime. A single warm execution environment can serve multiple concurrent requests, which reduces cold starts by orders of magnitude. You can stream results to clients incrementally with native HTTP streaming. You can create and configure Lambda Web Functions using the AWS Lambda API, Agent Toolkit for AWS, AWS Management Console, AWS CLI, AWS CloudFormation, AWS SAM, AWS CDK, or the AWS SDKs. With multi-Region routing, you can deploy once and serve each request from the Region closest to the end user. Web Functions support Amazon Virtual Private Cloud (VPC) connectivity, allowing your function to access resources that exist within a VPC. Lambda Web Functions is available today in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Tokyo), and Europe (Ireland). For the current list, see the AWS Capabilities by Region page. To learn more, see the Lambda Web Functions developer guide. For pricing details, see AWS Lambda Pricing.  To see Lambda Web Functions in action, see the AWS News Blog announcement.

Announcing consumption-based pricing for short-term memory in Amazon Bedrock AgentCore

Announcing consumption-based pricing for short-term memory in Amazon Bedrock AgentCore\nAmazon Bedrock AgentCore memory now prices short-term memory (STM) based on the data your agents ingest, retrieve, and store, replacing the previous flat rate per event. AgentCore memory gives agents in-session context through short-term memory and persistent knowledge across sessions through long-term memory. With consumption-based pricing, developers building agents that write frequent, small events pay in proportion to what they use. STM is priced across three dimensions: ingestion at $1.00 per GB, retrieval at $0.20 per GB, and storage at $0.10 per GB-month. Ingestion and retrieval are billed per event with a 12 KB minimum and 64 KB maximum billable size, while storage is billed on each event’s actual size. Most workloads see lower costs, with small-payload use cases seeing the largest reductions. You can manage storage costs by setting a shorter time to live (TTL) on your memory resources. Long-term memory pricing is unchanged. Consumption-based pricing for AgentCore Memory short-term memory is available in all AWS Regions where AgentCore Memory is offered. To get started, visit the Amazon Bedrock AgentCore pricing page.

AWS Transfer Family supports custom ports for public SFTP servers

AWS Transfer Family now lets you configure custom ports on public SFTP servers and choose a communication mode for each port. You can support clients that require different ports or start SFTP connections differently on a single server.\nPreviously, public SFTP servers accepted connections only on port 22 and used server-first communication. With this launch, you can configure up to three ports (22, or any port from 2000 to 65535), allowing you to use the port numbers your business or network requires and offer multiple ports on one server. Some SFTP clients initiate communication themselves, and their connections can occasionally stall on a server-first port. For each port, you can now select client-first or server-first communication and direct clients to the port that matches their connection behavior. Using a port other than 22 can also reduce unwanted connection attempts from automated scanners that target port 22. This capability is available in all AWS Regions where Transfer Family public SFTP servers are offered. To get started, visit the AWS Transfer Family console or use the AWS CLI or AWS SDKs. To learn more, visit Create an SFTP-enabled server in the AWS Transfer Family User Guide

AWS Secrets Manager adds managed external secrets support for Databricks and Twilio

AWS Secrets Manager now extends its managed external secrets capability to include Databricks and Twilio, enabling you to automatically rotate the credentials that your data pipelines and messaging applications use to call these services. You set up rotation directly from the AWS Management Console or API without writing or maintaining any custom rotation Lambda functions.\nLong-lived credentials often stay unrotated because rotation is hard to set up and hard to distribute safely to the applications that use the updated credential. With managed external secrets, you can use Secrets Manager to rotate the Databricks personal access tokens that your jobs and continuous integration and continuous delivery (CI/CD) pipelines use. You can also rotate the service principal OAuth client secrets that your automation workflows use. Each replacement personal access token retains the scopes of the token it replaces, so a narrowly scoped token stays narrowly scoped. For Twilio, you can use Secrets Manager to rotate the API keys and Auth Tokens that your applications use to send messages and place calls. For both services, you store the credential in Secrets Manager as a managed external secret and set a rotation schedule. Secrets Manager always returns the current credential, so your applications pick up rotated values automatically, with no code changes required. These integrations join existing managed external secrets support for BigID, Cisco Security Platform, Confluent Cloud, Datadog, GitLab, Jenkins, MongoDB Atlas, Netskope, Paddle, Salesforce, Snowflake, and SonarQube. Databricks and Twilio managed external secrets are available in all AWS Regions where AWS Secrets Manager managed external secrets is supported. To learn more, visit the  AWS Secrets Manager managed external secrets documentation.

Amazon EMR now supports native data quality checks in Apache Spark jobs

Amazon EMR now validates data quality natively within Apache Spark jobs. Data engineering teams can catch quality issues inline before bad data reaches downstream systems, without building or maintaining separate validation infrastructure. Amazon EMR Data Quality, powered by AWS Glue, is available at no additional cost and uses the same declarative rules language (Data Quality Definition Language) and APIs as AWS Glue for a consistent experience across both engines, and works with Apache Iceberg, Apache Hudi, and Delta Lake across EC2, EKS, and Serverless deployment modes.\nWith this feature, you can author validation rules in your Spark scripts or reference rules stored in the AWS Glue Data Catalog, and then apply them at any stage of your pipeline i.e., after reading data, during transformation, or after processing. With 30+ rule types, including dynamic rules that compare against historical runs and ML-based anomaly detection for tracking drift over time without specifying fixed thresholds, Amazon EMR Data Quality provides the flexibility of choosing the validation framework that fits your needs. You can take actions like failing a pipeline, filtering invalid records, or routing them to quarantine based on the quality evaluation results. Results are produced at the dataset, rule, and row level, so that you can easily identify which records or rules are driving the quality failures. Amazon EMR Data Quality is available on AWS runtime for Apache Spark (emr-spark-8.1.0) in all AWS Commercial, and AWS GovCloud (US) Regions where Amazon EMR is available. To get started, visit Amazon EMR Data Quality documentation and add DQDL rules for data quality validation to the Spark scripts of your EMR workloads.

Amazon ElastiCache for Valkey introduces ElastiCache Insights

Amazon ElastiCache for Valkey now features ElastiCache Insights, a pre-built dashboard in Amazon CloudWatch for your node-based clusters. ElastiCache Insights brings cluster health, memory, compute, network, command throughput, latency, keyspace, and errors into a single view sourced from a set of OpenTelemetry metrics that ElastiCache publishes automatically.\nYou can use ElastiCache Insights to check the health of your cache workloads across a Region at a glance, detect when a node nears its memory, network, or connection limits, and filter your view to a cluster, shard, node, or Availability Zone. You can also save a copy as the starting point for a custom dashboard tailored to your workload. With detailed monitoring, you can choose metrics to publish every 15 seconds and view them on the Metrics tab or in your custom dashboard. CloudWatch pricing for OpenTelemetry metrics applies to ElastiCache detailed monitoring. To get started, open the Metrics tab for a cluster in the Amazon ElastiCache console and choose View in ElastiCache Insights. You can also choose ElastiCache Insights under Infrastructure Monitoring in the Amazon CloudWatch console. ElastiCache Insights is available for node-based Valkey clusters in all AWS Regions where Amazon CloudWatch supports OpenTelemetry metrics. To learn more about ElastiCache Insights, see Monitoring ElastiCache with OpenTelemetry metrics in the Amazon ElastiCache User Guide.

Amazon OpenSearch Service now supports index-level encryption

Amazon OpenSearch Service now supports index-level encryption, letting you encrypt each index at rest with its own AWS Key Management Service (AWS KMS) customer managed key. This helps you protect data with different ownership or sensitivity under separate keys on the same domain. For example, you can encrypt each tenant’s data with its own key in a multi-tenant application.\nIndex-level encryption builds on the existing encryption at rest capability in Amazon OpenSearch Service. To enable it, set the encryption mode to Native and specify a customer managed AWS KMS key when you create a new domain using the AWS Management Console or the AWS CLI. You can then register additional keys with the domain and use them to encrypt individual indexes. Index-level encryption is available for domains running OpenSearch version 3.3 or later in 14 AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), Europe (Frankfurt, Ireland, London, Paris), and South America (São Paulo). There is no additional charge for the feature. Standard AWS KMS and OpenSearch Service charges apply. To get started, see Index-level encryption in the Amazon OpenSearch Service Developer Guide.

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