Kategorie: Amazon AWS

AWS Lambda durable functions are now available in AWS GovCloud (US) Regions

AWS Lambda durable functions are now available in the AWS GovCloud (US-West) and AWS GovCloud (US-East) Regions. Lambda durable functions enable developers to build reliable multi-step applications and AI workflows within the Lambda developer experience.
Lambda durable functions extend the Lambda programming model with new primitives in your event handler, such as “step” and “wait”, allowing you to checkpoint progress, automatically recover from failures, and pause executions without incurring compute charges for on-demand functions. With availability in the AWS GovCloud (US) Regions, government customers and regulated industries can orchestrate complex processes such as order workflows, user onboarding, and AI-assisted tasks while meeting stringent compliance and security requirements.
You can activate durable functions for new Python (versions 3.13 and 3.14), Node.js (versions 22 and 24), Go (versions 1.24 or later), .Net (version 2.2), or Java (17+) based Lambda functions using the AWS Lambda API, AWS Management Console, or AWS SDK. You can also use infrastructure as code tools such as AWS CloudFormation, AWS Serverless Application Model (AWS SAM), and the AWS Cloud Development Kit (AWS CDK).
For more information on durable functions, visit the AWS Lambda durable functions product page or the AWS Lambda Developer Guide. To learn about pricing, visit AWS Lambda pricing. For region availability, visit the AWS Capabilities by Region page. 
Quelle: aws.amazon.com

Amazon DynamoDB Streams now supports timestamp-based stream processing

Amazon DynamoDB Streams now supports timestamp-based stream processing, enabling you to begin reading stream records from a specific point in time. Previously, you could only start from the oldest retained record (up to 24 hours old) or from new records written after you start consuming the stream. Now, you can supply a timestamp and begin processing at the first record written at or after that time using the AT_TIMESTAMP shard iterator type.
Timestamp-based stream processing can be used with the Amazon Kinesis Client Library (KCL) through the DynamoDB Streams Kinesis adapter. It helps you avoid reprocessing entire retained streams or missing records when resuming stream consumption, supporting common use cases such as recovery after an interruption, data replay for debugging and auditing by reading from a specific point in the past, skipping a backlog by resuming from a recent timestamp near the latest records, and synchronizing after a restore by beginning at the export timestamp following a point-in-time recovery (PITR) export.
Timestamp-based stream processing is available in all commercial AWS Regions, the AWS GovCloud (US) Regions, and the China Regions. There is no additional charge for this feature beyond standard GetRecords API request charges. To get started, see Amazon DynamoDB Streams timestamp documentation in the DynamoDB developer guide.
Quelle: aws.amazon.com

Amazon EC2 T8i instances are now available in South America (São Paulo)

Amazon Elastic Compute Cloud (Amazon EC2) T8i instances are now available in South America (São Paulo). These instances are powered by custom sixth generation Intel Xeon 6 processors, available exclusively on AWS, and built on the latest sixth generation AWS Nitro cards. T8i instances deliver up to 30% better price performance compared to previous generation T3 instances, and offer four sizes: nano, micro, small, and medium. These instances are designed for workloads requiring low to moderate CPU utilization, including data processing, login gateways, small databases, batch processing, event driven functions, CI/CD pipelines, microservices, and low traffic websites.

T8i instances deliver up to 70% higher compute performance, 1.25x higher network bandwidth, and 2.4x higher EBS bandwidth compared to T3 instances. They use the same CPU credit system as T3 with Standard and Unlimited modes, so existing T3 customers can upgrade to T8i and immediately benefit from better price performance, enabling them to lower their TCO. For customers new to AWS or migrating from on-premises, T8i instances provide one of the most cost-effective entry points to run a variety of workloads.

T8i.micro and T8i.small are available under the AWS Free Tier. T8i instances are available in US East (N. Virginia, Ohio), US West (Oregon, N. California), Europe (Frankfurt, Ireland, London, Paris), Asia Pacific (Hyderabad, Malaysia, Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central) and South America (São Paulo) regions. T8i instances are available to purchase via On-Demand instances, and Spot instances with Savings Plan option coming soon. To learn more, visit the Amazon EC2 T8i instances page.
Quelle: aws.amazon.com

Amazon SageMaker Studio simplifies getting started for new AI/ML builders with new sign-in options

Amazon SageMaker Studio now lets new customers sign up with their Google, GitHub, or Apple credentials and land inside a fully configured Studio environment – no traditional AWS account setup and no console navigation required.
Previously, new customers who discovered a model on Hugging Face or an AWS product page had to complete a multi-step AWS account creation process, provide a credit card, navigate the AWS Console to find SageMaker AI, configure an environment, and set up IAM permissions before they could begin working. With earlier launches, we reduced environment setup to twenty seconds, pre-configured model customization permissions, and introduced deep links from Hugging Face and AWS product pages so customers could land directly inside Studio with a model pre-loaded. Now, with new sign-in options, customers complete a simplified sign-up using an existing identity, receive free credits as part of the AWS Free Tier with no credit card required in most cases, and arrive inside a ready-to-use SageMaker Studio environment where they can immediately fine-tune, evaluate, and deploy foundation models. Customers can invite team members by email, and collaborators sign in with their own email credentials to access the shared Studio environment, including shared models, notebooks, and endpoints without configuring IAM users or permissions. When a project grows beyond free credits, customers can upgrade to a paid plan and set a spend limit to stay within budget. Verified customers continue to receive default GPU access to G5, G6, G6e, and G4dn instances across endpoint deployments, training jobs, and notebooks without requesting quota increases.
This feature is available in all AWS Commercial Regions where Amazon SageMaker Studio is supported. To get started, visit any supported model on Hugging Face and select “Customize and Deploy on SageMaker AI” or “Deploy on SageMaker AI,” or click Get Started from the SageMaker Studio page. To learn more, see the AWS sign-up documentation and SageMaker Studio setup guide.
Quelle: aws.amazon.com

Web Search on Amazon Bedrock AgentCore expands to AWS GovCloud and 3 Regions

Web Search on Amazon Bedrock AgentCore is now available in four additional AWS Regions: US East (Ohio), US West (Oregon), Asia Pacific (Sydney), and AWS GovCloud (US-West). Web Search is a fully managed tool that provides AI agents with current grounding from the web, enabling more accurate and up-to-date responses for tasks such as research, summarization, and question answering.
With this expansion, customers can build agents that retrieve web information closer to their applications and end users with lower latency. Customers with government workloads can now use Web Search in the AWS GovCloud (US-West) Region to meet compliance and data residency requirements. 
To learn more about Web Search on Amazon Bedrock AgentCore, see the Web Search tool documentation. To learn about pricing, visit Amazon Bedrock AgentCore pricing. For Region availability, visit Supported AWS Regions.
Quelle: aws.amazon.com

Amazon Time Sync Service adds support for microsecond-accurate time on 135 additional EC2 instance types in all commercial regions

Amazon Time Sync Service introduces support for microsecond-accurate time on 135 additional EC2 instance types—spanning 12 instance families—in all commercial regions. Built on Amazon’s proven network infrastructure and the AWS Nitro System, microsecond-accurate time and nanosecond-precision hardware timestamps leverage the reference clocks running in the Nitro System directly, enabling customers to easily order application events, measure 1-way network latency, and increase distributed application transaction speed.
The newly supported families include eight Gen8 families (C8a, C8i, C8i-flex, C8id, M8i, M8i-flex, R8a, and X8i) and four Gen9 families (C9g, C9gd, M9g, and M9gd).
Starting today, customers can access microsecond-accurate time on these additional instance types by creating a Precision Time Placement Group (PTPG), a new placement strategy that allows customers to launch instances with Precision Time Protocol hardware clock (PHC) enabled. Customers that require both low network latency as well as precision time can associate a PTPG with their Cluster Placement Group (CPG), so that their low-latency workloads also benefit from microsecond-accurate time.
For more information, refer to the Amazon Time Sync Service documentation.
Quelle: aws.amazon.com

AWS HealthOmics now supports shared VPCs

AWS HealthOmics now supports sharing VPCs across accounts, enabling your workflows to access resources in Amazon Virtual Private Clouds (VPCs) managed by other AWS accounts with AWS Resource Access Manager (RAM). AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows.
With shared VPCs, customers can centralize networking administration by sharing subnets and security groups from a single VPC across multiple accounts in their organization, and HealthOmics then places each account’s workflows directly into those shared subnets. With cross-account VPC sharing, multiple accounts run inside one centrally managed VPC, instead of duplicating endpoints across VPCs. Workflows in participating accounts can access data privately without separate VPC peering connections or duplicated VPC endpoints. Previously, customers with centralized networking had to create separate AWS PrivateLink VPC endpoints in each VPC and maintain individual route tables using VPC peering. Cross-account VPC sharing reduces infrastructure overhead and simplifies network management for organizations with centralized networking while allowing researchers and developers to run bioinformatics workflows in their own accounts.
Shared VPCs is available in all Regions where AWS HealthOmics is available: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Singapore, Seoul, Tokyo). To get started, see the AWS HealthOmics User Guide.
Quelle: aws.amazon.com

Muse Spark Open model now available on Amazon SageMaker JumpStart

Meta’s Muse Spark Open is now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This coding-optimized open-weight model from Meta is purpose-built for agentic coding workflows, bringing multi-step reasoning and tool calling to enterprise software engineering on AWS infrastructure.
Muse Spark Open is designed for long-running coding agents and multimodal engineering workflows. Built on a Mixture-of-Experts (MoE) architecture with 816B total parameters and 42B active per token, it delivers the capacity of a very large network while keeping per-token compute closer to that of a much smaller dense model. The model holds a 1M-token context window, enabling it to ingest an entire project—including dependencies, legacy code, and thousands of files—without splitting tasks into smaller pieces. It accepts text, images, documents, audio, and audio-video as input and generates text output. Trained on real developer workflows such as multi-file refactors and long debugging sessions, it plans, acts, observes results, and self-corrects over long horizons rather than making single tool calls. SageMaker JumpStart offers two variants: BF16 (Muse-Spark-Open-816B-A42B) and NVFP4 quantized (Muse-Spark-Open-816B-A42B-NVFP4), with the NVFP4 variant requiring roughly a quarter of the memory for cost-efficient deployment.
With SageMaker JumpStart, customers can deploy Muse Spark Open with just a few clicks, with the endpoint running in their own AWS account for full control over source code and prompts.
To get started with this model, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

AWS Lambda Durable Execution SDK for Go is now generally available

Today, AWS announces the general availability of the AWS Lambda Durable Execution SDK for Go, enabling Go developers to build resilient, long-running workflows using Lambda durable functions. With this SDK, developers can create multi-step applications like infrastructure automation, deployment rollouts with approval gates, and pipelines that call AI models and pause for human review directly in their Go code without implementing custom progress tracking.
Lambda durable functions extend Lambda’s event-driven programming model with operations that checkpoint progress automatically and pause execution for up to an year when waiting on external events. The AWS Lambda Durable Execution SDK for Go provides a native Go experience for building with Lambda durable functions. It includes steps for progress tracking, callback integration for human and agent-in-the-loop workflows, durable invocation for reliable function chaining, and waits for efficient suspension.
The SDK works with the Go tools developers already use. Developers add it to a Go module with go get and deploy their function as a single static binary on the provided.al2023 runtime, like any other Go function on Lambda. Before deploying, they can locally test complete workflows on their own machine with the durabletest package, without requiring network access or AWS credentials, and use the durablelint analyzer to catch code that could break a workflow when it resumes.
To get started, see the Lambda durable functions developer guide and the AWS Lambda Durable Execution SDK for Go on GitHub. For Regional availability and pricing details for Lambda durable functions, see the AWS Capabilities by Region page and AWS Lambda Pricing.
Quelle: aws.amazon.com

Amazon DynamoDB Streams now supports timestamp-based stream processing

Amazon DynamoDB Streams now supports timestamp-based stream processing, enabling you to begin reading stream records from a specific point in time. Previously, you could only start from the oldest retained record (up to 24 hours old) or from new records written after you start consuming the stream. Now, you can supply a timestamp and begin processing at the first record written at or after that time using the AT_TIMESTAMP shard iterator type.
Timestamp-based stream processing can be used with the Amazon Kinesis Client Library (KCL) through the DynamoDB Streams Kinesis adapter. It helps you avoid reprocessing entire retained streams or missing records when resuming stream consumption, supporting common use cases such as recovery after an interruption, data replay for debugging and auditing by reading from a specific point in the past, skipping a backlog by resuming from a recent timestamp near the latest records, and synchronizing after a restore by beginning at the export timestamp following a point-in-time recovery (PITR) export.
Timestamp-based stream processing is available in all commercial AWS Regions, the AWS GovCloud (US) Regions, and the China Regions. There is no additional charge for this feature beyond standard GetRecords API request charges. To get started, see Amazon DynamoDB Streams timestamp documentation in the DynamoDB developer guide.
Quelle: aws.amazon.com