10/20/2026, 12:00:00 AM ~ 10/21/2026, 12:00:00 AM (UTC)

Recent Announcements

Polly Generative engine upgraded with more natural speech, 14 new voices, and 2 new languages

Today, we are introducing a new model behind the Amazon Polly Generative engine that delivers more natural-sounding, accurate speech output with low latency. With this upgrade, you get access to an enhanced voice portfolio with higher overall quality, 14 new voices across nine locales, and two new languages—Hindi and Finnish—now generally available on the Generative engine for the first time. New voices include Kenneth and Nate (US English), Russell and Curtis (Australian English), Kiara and Arjun (Hindi and Indian English), Tina (German), Carlos (US Spanish), Leo and Carolina (Brazilian Portuguese), Hyunduk (Korean), and Suvi (Finnish).\nAmazon Polly is a fully managed service that turns text into natural-sounding speech. This drop-in model upgrade makes Generative voices more natural and accurate. Complex content such as numbers, email addresses, and street addresses is designed to sound more reliable, reducing the need to fine-tune speech output. You can continue to use Speech Synthesis Markup Language (SSML) tags to control speech output on the Generative engine, just as before. The low-latency streaming API makes it easy to build contact center experiences, chatbots, and large language model (LLM) based agentic voice applications for real-time conversations. The richness and variety of voice options are also well suited for news, media, entertainment, e-learning, and accessibility use cases. This launch also expands the Amazon Polly Generative offering to AWS GovCloud, providing more options for public sector and regulated industries. All new Generative voices are now available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Europe (Frankfurt), Europe (Zurich), Europe (London), Asia Pacific (Sydney), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Tokyo), Canada (Central), and AWS GovCloud (US-West). To hear how Polly voices sound, go to Amazon Polly Features. For more details on the Polly offerings and use, see the Amazon Polly documentation and pricing page.

AWS announces TorchNeuron: Native PyTorch Support for AWS Trainium (Public Beta)

Today, AWS launched the public beta of TorchNeuron, an open source native PyTorch backend for AWS Trainium. Trainium is a portfolio of AI chips designed to deliver high performance and cost efficiency for deep learning AI training and inference workloads. TorchNeuron enables AI scientists and researchers to experiment and build models with PyTorch natively on Trainium.\nWith TorchNeuron, developers can enable their PyTorch code on Trainium simply by switching to .to(’neuron’) while maintaining full PyTorch compatibility with eager execution, torch.compile, and standard PyTorch APIs. Additionally, TorchNeuron integrates with Neuron Kernel Interface (NKI) to enable custom operators and performance optimization for Trainium hardware. TorchNeuron is available as open source through the TorchNeuron github repo for Amazon EC2 Trainium2 instances and Amazon EC2 Trainium3 instances. To learn more about TorchNeuron and AWS Trainium accelerators, visit the AWS Neuron page.

Structured outputs for Claude models available on Amazon Bedrock

Amazon Bedrock now supports structured outputs for all Anthropic Claude models across both the bedrock-runtime and the bedrock-mantle endpoint. Whether you call Claude through boto3 and the bedrock-runtime APIs or through the Messages API and the bedrock-mantle endpoint, you can now receive schema-compliant JSON responses that conform to a defined structure, without custom parsing, retry logic, or post-processing.\nWith structured outputs for Claude on Amazon Bedrock, you can automate reliable, schema-compliant tasks across industries. Examples include extracting every required field from financial earnings reports and compliance documents, parsing clinical notes into Electronic Health Records (EHR), and enriching product catalogs with consistent, validated results. You can also analyze contracts for key terms, parties, and obligations, or power intelligent support ticket routing where intents, sentiments, and entities always match your application’s data model. To learn more, see the Amazon Bedrock documentation . Structured outputs is available where claude models are available. For more information check the regional availability details page.

AWS Clean Rooms now supports Databricks as a data source

AWS Clean Rooms now supports Databricks as a data source, so collaboration members and their partners can securely analyze data managed in Databricks without moving or revealing the source data. Members query a Databricks table shared through Delta Sharing directly in a collaboration. Databricks joins AWS Glue, Snowflake, and Amazon Athena as supported data sources in AWS Clean Rooms.\nPreviously, members with data in Databricks had to export or replicate it into Amazon S3 to use it as a table in a collaboration, which added pipeline work and an extra copy of the data to govern. Now members can register a Databricks table directly by providing the workspace URL, schema, and table along with a Delta Sharing share, and make it available to a collaboration. For example, a retailer that manages its customer data in Databricks can collaborate with a media partner on campaign measurement in AWS Clean Rooms, running approved analyses against its Databricks data without first building and maintaining an export to S3. AWS Clean Rooms helps companies and their partners easily analyze and collaborate on their collective datasets without revealing or copying one another’s underlying data. For more information about the AWS Regions where AWS Clean Rooms is available, see the AWS Regions table. To learn more about collaborating with AWS Clean Rooms, visit AWS Clean Rooms.

AWS IAM Identity Center simplifies identity source changes from Active Directory

AWS IAM Identity Center now retains your workforce identities and their assignments when you change your identity source from Active Directory to an external identity provider (IdP), such as Okta or Microsoft Entra ID, or to the Identity Center directory. Specifically, it preserves your users, groups, group memberships, permission set assignments, and application assignments. Previously, this change required you to recreate them in IAM Identity Center.\nIAM Identity Center helps you connect your workforce identities to AWS once and manage their access centrally across AWS applications and accounts. When you change your identity source from Active Directory, your existing identity store remains in place, so your users and groups keep the same IDs. As a result, users retain their access to AWS accounts and applications, and policies that reference your user IDs, group IDs, or identity store ID continue to work. To make this change, configure your new identity source with the standard setup required for users to sign in. After the change, your workforce signs in through your dual-stack AWS access portal URL. Changing your identity source in the other direction, to Active Directory, works as before. This feature is available for organization instances of IAM Identity Center in all AWS Regions where IAM Identity Center is available. IAM Identity Center is provided at no additional cost. To get started, navigate to the IAM Identity Center console. For more information, see Change your identity source in the IAM Identity Center User Guide. To learn more about IAM Identity Center, visit the product detail page.

AWS Partner Central launches Program Navigator, Case Study Generator, and streamlined specialization review

AWS Partner Central now features Program Navigator, a unified experience that lets partners discover, apply for, and track AWS Specialization program applications in one place. Partners can browse specialization designations, view requirements and benefits, and submit applications through a guided, step-by-step workflow.\nThe new Case Study Generator in AWS Partner Central gives partners a redesigned creation experience with three starting paths: build a case study directly with AI assistance, import from an existing customer success story URL, or auto-populate from an opportunity. Case studies created for go-to-market purposes now follow a streamlined review path, a lightweight policy validation that lets partners publish directly to AWS domains without a specialization review. The deeper specialization-level review applies only to case studies associated with Program Navigator applications. Additionally, AWS has streamlined the specialization application review process through Program Navigator. Partners submitting applications can expect faster review times and more detailed, requirement-level feedback throughout the process. Partners applying for non-specialization programs continue to access those programs through the existing experience. Program Navigator and Case Study Generator are available to all AWS Partners in AWS Partner Central. To get started, navigate to Program Navigator and Case Studies under the Go to Market section in the left navigation panel.

Amazon Bedrock Guardrails now supports Standard tier for contextual grounding checks

Amazon Bedrock Guardrails now offers a Standard tier for contextual grounding checks, which detect and filter hallucinations by checking whether a model response is grounded in your reference source and relevant to the user’s query. The Standard tier is more robust than the Classic tier. It supports responses that include reasoning or conversational content, scores differently worded responses with the same meaning more consistently, and provides more comprehensive language support, matching the Standard tier for content filters and denied topics. Amazon Bedrock Guardrails provides configurable safeguards for generative AI applications on any foundation model through the ApplyGuardrail API.\nUntil now, contextual grounding checks applied strict grounding suited to summarization, paraphrasing, and question answering. With the Standard tier, you can also apply contextual grounding checks to conversational applications and to responses that reason over the source. You choose the tier for each guardrail in the Amazon Bedrock console or API. Existing guardrails keep the Classic tier until you change them. Pricing is the same for both tiers. To use the Standard tier, your guardrail must use cross-Region inference with a guardrail profile. The Standard tier for contextual grounding checks is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), Europe (Frankfurt), Europe (Ireland), Europe (Milan), Europe (Paris), Europe (Spain), Europe (Stockholm), and Asia Pacific (Tokyo). To learn more, see the Amazon Bedrock Guardrails documentation on contextual grounding checks and safeguard tiers, or visit the Amazon Bedrock Guardrails product page.

Amazon Bedrock Advanced Prompt Optimization now supports prompt shortening

Amazon Bedrock Advanced Prompt Optimization now supports prompt shortening, an optional setting that reduces the length of your prompt while maintaining or improving your evaluation scores. Advanced Prompt Optimization optimizes prompts for any model on Amazon Bedrock against your own evaluation criteria. Until now, optimization focused on quality alone. Optimized prompts were often longer than the original. Longer prompts raise input token costs and can increase time to first token, which matters at scale and in latency-sensitive applications.\nWhen you turn on prompt shortening for a prompt template in your input dataset, the optimizer reduces the prompt’s length. The optimized prompt is returned only when it is shorter than your original prompt and scores at least as high on your evaluation. Otherwise your original prompt is returned, so the setting never leaves you with a longer or lower-scoring prompt. Results include a compression ratio and the original and optimized token counts, in the output file and on the console results page. For example, a compression ratio of 0.7 means the optimized prompt uses 30% fewer tokens. Prompt shortening works with any evaluation method, including steering criteria, LLM-as-a-judge rubrics, and custom Lambda evaluators.     Prompt shortening is available in all AWS Regions where Advanced Prompt Optimization is available. To get started, enable prompt shortening for a prompt template in your input dataset and create an optimization job as usual. To learn more, see Shorten prompts in the Amazon Bedrock User Guide, the Amazon Bedrock pricing page, and the Amazon Bedrock product page.

AWS Transform for migrations expands support for existing landing zones

AWS Transform now supports more types of existing AWS environments when building landing zones for your migration, including AWS Control Tower landing zones and landing zones built with AWS Organizations. AWS Transform runs a foundation gap analysis of your current environment against AWS best practices and expands your landing zone on top of what’s already in your target accounts, so your team can start migrating sooner.\nAWS Transform also now supports an Infrastructure as Code (IaC)-only mode for building landing zones for your migration. In this mode, AWS Transform analyzes your organization, generates recommendations, and produces IaC tailored to your migration plan in AWS CDK, Terraform, or Landing Zone Accelerator (LZA) format. IaC-only mode is designed for organizations that prefer not to grant AWS Transform direct access to their management account. AWS Transform can now connect through a delegated administrator (DA) account instead. It also benefits customers who manage their own deployment pipeline and don’t need the agent to deploy automatically. The AWS Transform for migrations landing zone agent automates landing zone design end to end, including foundation setup, organizational unit (OU) and account structure, service control policies (SCPs), and workload accounts aligned to your migration planning and waves. You can deploy directly or generate IaC for your own pipeline. These new capabilities are available in all target AWS Regions supported by AWS Transform for migrations. To learn more, please visit the AWS Transform User Guide.

Amazon ElastiCache Serverless for Valkey now supports online migration with zero downtime

Amazon ElastiCache Serverless for Valkey now supports online migration from self-managed Valkey and Redis OSS on Amazon EC2 with zero downtime. Zero downtime means zero Errors returned to the client during migration, measured the same way the ElastiCache SLA measures availability. Your application continues reading and writing through cutover, and you do not need third-party replication tools.\nOnline migration runs in three steps. You prepare the migration, start the data sync, and complete the cutover. ElastiCache gives you a migration endpoint your application can point to when you prepare the migration, and data replicates continuously from your source once you start the sync. You choose when writes move to the serverless cache, with a manual cutover or an automatic one once your data is in sync. ElastiCache records each step as an event on the cache’s Events tab, with the reason for any failed step. Online migration connects to your source using encryption in transit (TLS) and authentication, and reads credentials from AWS Secrets Manager, so your existing security controls stay in place. Online migration supports self-managed caches in cluster mode on Amazon EC2 running Redis OSS 4.0 through 7.2 or Valkey 7.2 through 8.1, and you can migrate them straight to Valkey 9.0. Migration usage is free for the first 180 days per AWS account, starting when the account first prepares a migration. After that, you pay per node-hour for the migration nodes that ElastiCache runs to replicate your data, for as long as a migration is active. The ElastiCache Serverless cache is billed separately at standard data storage and ECPU rates, including during the free period. For pricing details, see Amazon ElastiCache pricing. Online migration is available in all commercial AWS Regions, the China Regions, and the AWS GovCloud (US) Regions.  To get started, create or select an empty ElastiCache Serverless cache to migrate into, then start the migration from the ElastiCache console or the AWS CLI. To learn more, see the documentation.

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