I have been workings with AWS from pass 1.5 year and my manager wants me to get a SAA role. It is required for my promotion. Need help to find the best resource and test suite to check my knowledge against as i am running out of time. I have already bought udemy course to start with. submitted by /u/Subject-Proof-7063 [link] [comments]

I just got the email that I passed the certification exam. I am wandering if when you post the cert to LinkedIn if you include the validation number or just provide it when a recruiter wants to verify your certificate? https://preview.redd.it/v0lgqoxyw35g1.png?width=783&format=png&auto=webp&s=199dedb59d5bf3d63852002c54483de5ba0e7d6a submitted by /u/coffee_software_dev [link] [comments]

Just enrolled today. Does anyone have any advice on where to go to for material? (I.e books, Youtube, tutorials for labs.. etc.) submitted by /u/ImplementOpen1492 [link] [comments]

Straight to the point. I have problem with the payment of the exam and contacted the support,*the agent asked for my visa card credentials *. I don't know how this is possible. The call was via verified Pearson number. Have anyone been through the same before? submitted by /u/Ok-Drive-9926 [link] [comments]

I’m taking the AWS MLA-C01 (Machine Learning Engineer – Associate) exam in the middle of this month, and I want to make sure I’m fully prepared for the OnVUE setup. I know I need my passport (this is the only valid ID I have), a quiet space, stable internet, and that I’ll have to re-download the OnVUE software on exam day, enter the code they give me, and re-check my camera/mic before starting. Besides these, is there anything else I should be concerned about, and how long did your exam results take to arrive (I’ve read around 5 days, but not sure)? submitted by /u/InsectActive95 [link] [comments]

https://www.reddit.com/r/AWSCertifications/s/45GQuoKRwa I have updated the GenAI Developer Pro exam resources post linked above as there are 3 new resources launched recently: Video course from Frank Kane & Stephane Maarek Practice exams from Frank Kane Practice exams from Stephane Maarek and Abhishek Singh These are in addition to the video course and practice exams from Tutorialsdojo that was already there submitted by /u/madrasi2021 [link] [comments]

Any AWS expert here? Need help. Case: We had a client account where days was saved in AWS server. The payment wasn't done and they have deleted the account. It's been more than 8-9 months.. Is there anyways to make the payment and get it back? Advice would be helpful. Thanks. submitted by /u/surfing_instructor [link] [comments]

I am following Adrain C's AWS Solutions Architect - Associate course. On their site, there is a reference to a Discord channel, but the invite link seems invalid. Does anyone know what the updated link is? Thanks! submitted by /u/thehermitcoder [link] [comments]

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Hello folks, I’ve just started learning AWS, and to learn by doing, I created a scenario for myself. I defined some basic requirements for a simple website, but I’m not sure what the correct order is before actually building the system. Should I start by drawing the architecture diagram first? Or should I define the requirements and then list the AWS services that match them? Or should I document everything after choosing the services? At which stage should I define the configurations? In what order should I approach computing, networking, database, storage, and security components? And lastly, which AWS documentation should I use to add real engineering value to what I’m building? Can you guide me through this? submitted by /u/Hefty_Mortgage_4027 [link] [comments]

Amazon SageMaker HyperPod now supports checkpointless training, a new foundational model training capability that mitigates the need for a checkpoint-based job-level restart for fault recovery. Checkpointless training maintains forward training momentum despite failures, reducing recovery time from hours to minutes. This represents a fundamental shift from traditional checkpoint-based recovery, where failures require pausing the entire training cluster, diagnosing issues manually, and restoring from saved checkpoints, a process that can leave expensive AI accelerators idle for hours, costing your organization wasted compute. Checkpointless training transforms this paradigm by preserving the model training state across the distributed cluster, automatically swapping out faulty training nodes on the fly and using peer-to-peer state transfer from healthy accelerators for failure recovery. By mitigating checkpoint dependencies during recovery, checkpointless training can help your organization save on idle AI accelerator costs and accelerate time. Even at larger scales, checkpointless training on Amazon SageMaker HyperPod enables upwards of 95% training goodput on cluster sizes with thousands of AI accelerators. Checkpointless training on SageMaker HyperPod is available in all AWS Regions where Amazon SageMaker HyperPod is currently available. You can enable checkpointless training with zero code changes using HyperPod recipes for popular publicly available models such as Llama and GPT OSS. For custom model architectures, you can integrate checkpointless training components with minimal modifications for PyTorch-based workflows, making it accessible to your teams regardless of their distributed training expertise. To get started, visit the Amazon SageMaker HyperPod product page and see the checkpointless training GitHub page for implementation guidance.

In May, we open sourced the Strands Agents SDK, an open source python framework that takes a model-driven approach to building and running AI agents in just a few lines of code. Today, we’re announcing that TypeScript support is available in preview. Now, developers can choose between Python and TypeScript for building Strands Agents. TypeScript support in Strands has been designed to provide an idiomatic TypeScript experience with full type safety, async/await support, and modern JavaScript/TypeScript patterns. Strands can be easily run in client applications, in browsers, and server-side applications in runtimes like AWS Lambda and Bedrock AgentCore. Developers can also build their entire stack in Typescript using the AWS CDK. We’re also announcing three additional updates for the Strands SDK. First, edge device support for Strands Agents is generally available, extending the SDK with bidirectional streaming and additional local model providers like llama.cpp that let you run agents on small-scale devices using local models. Second, Strands steering is now available as an experimental feature, giving developers a modular prompting mechanism that provides feedback to the agent at the right moment in its lifecycle, steering agents toward a desired outcome without rigid workflows. Finally, Strands evaluations is available in preview. Evaluations gives developers the ability to systematically validate agent behavior, measure improvements, and deploy with confidence during development cycles. Head to the Strands Agents GitHub to get started building.

Amazon Web Services (AWS) announces a new serverless model customization capability that empowers AI developers to quickly customize popular models with supervised fine-tuning and the latest techniques like reinforcement learning. Amazon SageMaker AI is a fully managed service that brings together a broad set of tools to enable high-performance, low-cost AI model development for any use case.  Many AI developers seek to customize models with proprietary data for improved accuracy, but this often requires lengthy iteration cycles. For example, AI developers must define a use case and prepare data, select a model and customization technique, train the model, then evaluate the model for deployment. Now AI developers can simplify the end-to-end model customization workflow, from data preparation to evaluation and deployment, and accelerate the process. With an easy-to-use interface, AI developers can quickly get started and customize popular models, including Amazon Nova, Llama, Qwen, DeepSeek, and GPT-OSS, with their own data. They can use supervised fine-tuning and the latest customization techniques such as reinforcement learning and direct preference optimization. In addition, AI developers can use the AI agent-guided workflow (in preview), and use natural language to generate synthetic data, analyze data quality, and handle model training and evaluation—all entirely serverless.  You can use this easy-to-use interface in the following AWS Regions: Europe (Ireland), US East (N. Virginia), Asia Pacific (Tokyo), and US West (Oregon). To join the waitlist to access the AI agent-guided workflow, visit the sign-up page.  To learn more, visit the SageMaker AI model customization page and blog.

I'm a web developer currently learning full stack development. My technology is MERN stack and and keeping AI in mind I'll be developing some AI projects in future so which AWS certification will be best for me to do currently. I'm new to AWS but I want to learn about its different services and mostly EC2 and other, something that'll help me in securing a job as a full stack developer and also help me in building a scalable AI web-apps. submitted by /u/clever-coder [link] [comments]

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Hey everyone, I passed my CCP about two months ago and immediately started preparing for SAA-C03. But for some reason, I still don’t feel prepared. I’ve been doing mocks from Stephane Maarek and TDJ. The results are confusing — some mocks I pass, others I end up around 69–70%. I even re-attempted all the tests and took detailed notes on every wrong answer. My exam is in 2 days and I really don’t want to postpone it. How should I prepare in these last 48 hours? What should I focus on to feel confident before the exam? Any tips from people who recently cleared SAA-C03 would be really helpful! submitted by /u/speocket [link] [comments]

Amazon SageMaker HyperPod now supports elastic training, enabling organizations to accelerate foundation model training by automatically scaling training workloads based on resource availability and workload priorities. This represents a fundamental shift from training with a fixed set of resources, as it saves hours of engineering time spent reconfiguring training jobs based on compute availability. Any change in compute availability previously required manually halting training, reconfiguring training parameters, and restarting jobs—a process that requires distributed training expertise and leaves expensive AI accelerators sitting idle during training job reconfiguration. Elastic training automatically expands training jobs to absorb idle AI accelerators and seamlessly contracting when higher-priority workloads need resources—all without halting training entirely. By eliminating manual reconfiguration overhead and ensuring continuous utilization of available compute, elastic training can help save time previously spent on infrastructure management, reduce costs by maximizing cluster utilization, and accelerate time-to-market. Training can start immediately with minimal resources and grow opportunistically as capacity becomes available. SageMaker HyperPod is available in all regions where Amazon SageMaker HyperPod is currently available. Organizations can enable elastic training with zero code changes using HyperPod recipes for publicly available models including Llama and GPT OSS. For custom model architectures, customers can integrate elastic training capabilities through lightweight configuration updates and minimal code modifications, making it accessible to teams without requiring distributed systems expertise. To get started, visit the Amazon SageMaker HyperPod product page and see the elastic training documentation for implementation guidance.

Amazon Bedrock now supports reinforcement fine-tuning, helping you improve model accuracy without needing deep machine learning expertise or large sums of labeled data. Amazon Bedrock automates the reinforcement fine-tuning workflow, making this advanced model customization technique accessible to everyday developers. Models learn to align with your specific requirements using a small set of prompts rather than the large sums of data needed for traditional fine-tuning methods, enabling teams to get started quickly. This capability teaches models through feedback on multiple possible responses to the same prompt, improving their judgement of what makes a good response. Reinforcement fine-tuning in Amazon Bedrock delivers 66% accuracy gains on average over base models so you can use smaller, faster, and more cost-effective model variants while maintaining high quality. Organizations struggle to adapt AI models to their unique business needs, forcing them to choose between generic models with average performance or expensive, complex customization that requires specialized talent, infrastructure, and risky data movement. Reinforcement fine-tuning in Amazon Bedrock removes this complexity by making advanced model customization fast, automated, and secure. You can train models by uploading training data directly from your computer or choose from datasets already stored in Amazon S3, eliminating the need for any labeled datasets. You can define reward functions using verifiable rule-based graders or AI-based judges along with built-in templates to optimize your models for both objective tasks such as code generation or math reasoning, and subjective tasks such as instruction following or chatbot interactions. Your proprietary data never leaves AWS's secure, governed environment during the entire customization process, mitigating security and compliance concerns. You can get started with reinforcement fine-tuning in Amazon Bedrock through the Amazon Bedrock console and via the Amazon Bedrock APIs. At launch, you can use reinforcement fine-tuning with Amazon Nova 2 Lite with support for additional models coming soon. To learn more about reinforcement fine-tuning in Amazon Bedrock, read the launch blog, pricing page, and documentation.

In total: 3 months of preparation. Tutorials dojo+youtube In Sep have passed associate too for training purposes. The most difficult part was to actually sit behind the laptop for 3 hours and not to loose my focus. submitted by /u/shurikskr [link] [comments]

been using aws for more than 8 years and i just decided to take certification today and i started with CCP. i took Stepane's course in udemy last night and i didnt even finish it. I took his Practice exam and failed at 60% lol. i most likely got confused with the wordings so i reviewed the mock exam I was told theres no physical certificate for CCP. and i could not see my score after the exam. just says PASS. is this the same for everyone? also it will reflect in my account after 5 days. My next target is the SA Associate. and i will have to really prepare for this. EDIT: Man.. doesnt matter if you failed the mock exam or you didnt memorize all the AWS services. a Pass is a Pass submitted by /u/linux_n00by [link] [comments]

Hi everyone, I am working in a non-tech industry, but have a math B.S. in the U.S. I am contemplating studying for entry-level AWS certifications, with the end goal of taking the Solutions Architect Pro test. I have no prior experience in IT but I have good self-studying skills. Is it possible to self-study to pass the Solutions Architect Pro exam? How much real-experience do I need to be employable in the field? Can I get freelance/part-time jobs to get such experience? Thank you! submitted by /u/Accomplished_Desk769 [link] [comments]

Barely passed, it was indeed the most difficult certification exam I took. It’s very important to know all the services listed on the exam guide inside out. Try to plan your time accordingly, I had no time to go back and check my flagged questions and I flagged like 30 of them… submitted by /u/Well_that_suckss [link] [comments]

Hello folks, I work in AWS (Billing) Support and I have none of the certifications... What would be the best path to follow knowing the market is moving to AI? I thought about going for the AI certs, but honestly I have pretty much 0 tech knowledge and don't want to set myself for failure. Any recommendations? Thanks! submitted by /u/Kikabrenes [link] [comments]

Just passed AIF! Now I have 2 certs SAA and AIF. My plan is to knock out 2-3 really good projects then moving on to MLA followed by GenAi Pro. Hopefully between the projects, certs, and experience I will break into cloud. I have 3 years of Telecom PM Exp, 6 months Telecom SE, and 1 year Tier 1 HD. Also finishing up my BS in IT this coming March. Exam took me about 2 weeks to pass with no prior AI experience other than lots of ChatGPT usage. I used Stephan’s Udemy course and TD exams. Quick fire questions an info from ChatGPT as well. submitted by /u/StinkyFeet4Days [link] [comments]

Hi everyone! I'm currently preparing for AWS certifications and I've been considering using educative.io's practice labs. I've seen some mentions of it in forums, but I wanted to get more specific feedback from this community. Has anyone used their labs for AWS certification prep? How would you rate them? Are they realistic to the actual exam? What are the pros and cons compared to other resources like A Cloud Guru, Linux Academy, or practice tests from AWS directly? I'm specifically interested in preparing for the Solutions Architect Associate (SAA-C03) certification. Any insights would be greatly appreciated! Thanks in advance! submitted by /u/Acanthopterygii_Fit [link] [comments]

First, my skillbuilder session expire after a few hours. I ahve to log in again. But what is worse is that I cannot get aws skill builder to remember my credentials, each time I must fully re-type my login and password. Any suggestions? submitted by /u/keyboardwarrriorr [link] [comments]

I take the Solutions Architect Associate cert 6 months ago and am studying for the Security Exam for 2 months. I solved the official exam questions and did 9/10. Should I take the exam? submitted by /u/socodes [link] [comments]

Starting today, new general purpose high-frequency high-network Amazon Elastic Compute Cloud (Amazon EC2) M8azn instances are available for preview. These instances are powered by fifth generation AMD EPYC (formerly code named Turin) processors, offering the highest maximum CPU frequency, 5GHz in the cloud. The M8azn instances offer up to 2x compute performance versus previous generation M5zn instances. These instances also deliver 24% higher performance than M8a instances. M8azn instances are built on the AWS Nitro System, a collection of hardware and software innovations designed by AWS. The AWS Nitro System enables the delivery of efficient, flexible, and secure cloud services with isolated multitenancy, private networking, and fast local storage. These instances are ideal for applications such as gaming, high-performance computing, high-frequency trading (HFT), CI/CD, and simulation modeling for the automotive, aerospace, energy, and telecommunication industries. To learn more or request access to the M8azn instances preview, visit the Amazon EC2 M8a page.

I passed the SAP-C02 this week. Wanted to share my experience. Background: I’ve been working with AWS for about 3 years, mostly at the Associate level. Study Time: About 4~6 months of study Exam Experience: It’s as hard as everyone says. The questions are long, and most have two “correct” answers where you have to choose the BEST one. Flag for review and manage your time—I used all of it. Focus on trade-offs (cost vs. performance vs. complexity), migration pathways, and security/network boundary design. Thank You: A huge shoutout to the this community—lurking here gave me great insights. My #1 Tip: Do not take this exam without grinding through the practice tests. They expose the gaps in your knowledge perfectly. Happy to answer any specific questions. Good luck to everyone studying! https://preview.redd.it/9qxfm0j55t4g1.jpg?width=1050&format=pjpg&auto=webp&s=3fe427f1b4934208159dbe4b49dd5791e92f4d52 submitted by /u/thukhakyawe [link] [comments]

Today, Amazon announces the availability of Amazon Nova 2 Sonic, our speech-to-speech model for natural, real-time conversational AI.  It offers best-in-class streaming speech understanding with robustness to background noise and users’ speaking styles, efficient dialog handling, and speech generation with expressive voices that can speak natively in multiple languages (Polyglot voices). It has superior reasoning, instruction following, and tool invocation accuracy over the previous model. Nova 2 Sonic builds on the capabilities introduced in the original Nova Sonic model with new features including expanded language support (Portuguese and Hindi), polyglot voices that enable the model to speak different languages with native expressivity using the same voice, and turn-taking controllability to allow developers to set low, medium, or high pause sensitivity. The model also adds cross-modal interaction, allowing users to seamlessly switch between voice and text in the same session, asynchronous tool calling to support multi-step tasks without interrupting conversation flow, and a one-million token context window for sustained interactions. Developers can integrate Nova Sonic 2 directly into real-time voice systems using Amazon Bedrock’s bidirectional streaming API. Nova Sonic 2 now also seamlessly integrates with Amazon Connect and other leading telephony providers, including Vonage, Twilio, and AudioCodes, as well as open source frameworks such as LiveKit and Pipecat. Amazon Nova 2 Sonic is available in Amazon Bedrock in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Tokyo), and Europe (Stockholm). To learn more, read the AWS News Blog and the Amazon Nova Sonic User Guide. To get started with Nova Sonic 2 in Amazon Bedrock, visit the Amazon Bedrock console.

AWS announces the Apache Spark upgrade agent, a new capability that accelerates Apache Spark version upgrades for Amazon EMR on EC2 and EMR Serverless. The agent converts complex upgrade processes that typically take months into projects spanning weeks through automated code analysis and transformation. Organizations invest substantial engineering resources analyzing API changes, resolving conflicts, and validating applications during Spark upgrades. The agent introduces conversational interfaces where engineers express upgrade requirements in natural language, while maintaining full control over code modifications. The Apache Spark upgrade agent automatically identifies API changes and behavioral modifications across PySpark and Scala applications. Engineers can initiate upgrades directly from SageMaker Unified Studio, Kiro CLI or IDE of their choice with the help of MCP (Model Context Protocol) compatibility. During the upgrade process, the agent analyzes existing code and suggests specific changes, and engineers can review and approve before implementation. The agent validates functional correctness through data quality validations. The agent currently supports upgrades from Spark 2.4 to 3.5 and maintains data processing accuracy throughout the upgrade process. The Apache Spark upgrade agent is now available in all AWS Regions where SageMaker Unified Studio is available. To start using the agent, visit SageMaker Unified Studio and select IDE Spaces or install the Kiro CLI. For detailed implementation guidance, reference documentation, and migration examples, visit the documentation.

Amazon Relational Database Service (Amazon RDS) for SQL Server now offers Microsoft SQL Server 2022 Developer Edition. SQL Server Developer Edition is a free edition of SQL Server that contains all the features of Enterprise Edition and can be used in any non-production environment. This enables customers to build, test, and demonstrate applications using SQL Server while reducing costs and maintaining consistency with their production database configurations. Previously, customers that created Amazon RDS for SQL Server instances for development and test environments had to use SQL Server Standard Edition or SQL Server Enterprise Edition, which resulted in additional database licensing costs for non-production usage. Now, customers can lower the cost of their Amazon RDS development and testing instances by using SQL Server Developer Edition. Furthermore, Amazon RDS for SQL Server features such as automated backups, automated software updates, monitoring, and encryption for development and testing purposes will work on Developer Edition. The license for Microsoft SQL Server Developer Edition strictly limits its use to development and testing purposes. It cannot be used in a production environment, or for any commercial purposes that directly serve end-users. For more information, refer to the Amazon RDS for SQL Server User Guide. See Amazon RDS for SQL Server Pricing for pricing details and regional availability. 

Amazon Web Services is announcing the preview of Amazon EC2 X8i, next-generation Memory optimized instances. X8i instances are powered by custom Intel Xeon 6 processors delivering the highest performance and fastest memory among comparable Intel processors in the cloud. X8i instances offer 1.5x more memory capacity (up to 6TB) , and up to 3.4x more memory bandwidth compared to previous generation X2i instances. X8i instances will be SAP-certified and deliver 46% higher SAPS compared to X2i instances, for mission-critical SAP workloads. X8i instances are a great choice for memory-intensive workloads, including in-memory databases and analytics, large-scale traditional databases, and Electronic Design Automation (EDA). X8i instances offer 35% higher performance than X2i instances with even higher gains for some workloads. To learn more or request access to the X8i instances preview, visit the Amazon EC2 X8i page.

You can now attach Amazon S3 Access Points to your Amazon FSx for NetApp ONTAP file systems so that you can access your file data as if it were in S3. With this new capability, your file data in FSx for NetApp ONTAP is effortlessly accessible for use with the broad range of artificial intelligence, machine learning, and analytics services and applications that work with S3 while your file data continues to reside in your FSx for NetApp ONTAP file system. Amazon FSx for NetApp ONTAP is the first and only complete, fully managed NetApp ONTAP file system in the cloud, allowing you to migrate on-premises applications that rely on NetApp ONTAP or other NAS appliances to AWS without having to change how you manage your data. An S3 Access Point is an endpoint that helps control and simplify how different applications or users can access data. Now, with S3 Access Points for FSx for NetApp ONTAP, you can discover new insights, innovate faster, and make even better data-driven decisions with the data you migrate to AWS. For example, you can use your data to augment generative AI applications with Amazon Bedrock, train machine learning models with Amazon SageMaker, run analysis using Amazon Glue or a wide range of AWS Data and Analytics Competency Partner solutions, and run workflows using S3-based cloud-native applications. Get started with this capability by creating and attaching S3 Access Points to new FSx for NetApp ONTAP file systems using the Amazon FSx console, the AWS Command Line Interface (AWS CLI), or the AWS Software Development Kit (AWS SDK). Support for existing FSx for NetApp ONTAP file systems will come in an upcoming weekly maintenance window. This new capability is available in the select AWS Regions. To get started, see the following list of resources: Amazon FSx for NetApp ONTAP Amazon S3 Access Points AWS News Blog

Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6e-GB300 UltraServers. P6e-GB300 UltraServers, accelerated by NVIDIA GB300 NVL72, provide 1.5x GPU memory and 1.5x FP4 compute (without sparsity) compared to P6e-GB200.  Customers can optimize performance for the most powerful models in production with P6e-GB300 for applications that require higher context and implement emerging inference techniques like reasoning and Agentic AI. To get started with P6e-GB300 UltraServers, please contact your AWS sales representative. To learn more about P6e UltraServers and instances, visit Amazon EC2 P6 instances.

Amazon S3 Storage Lens provides organization-wide visibility into your storage usage and activity to help optimize costs, improve performance, and strengthen data protection. Today, we are adding three new capabilities to S3 Storage Lens that give you deeper insights into your S3 storage usage and application performance: performance metrics that provide insights into how your applications interact with S3 data, analytics for billions of prefixes in your buckets, and metrics export directly to S3 Tables for easier querying and analysis. We are adding three specific types of performance metrics. Access pattern metrics identify inefficient requests, including those that are too small and create unnecessary network overhead. Request origin metrics, such as cross-Region request counts, show when applications access data across regions, impacting latency and costs. Object access count metrics reveal when applications frequently read a small subset of objects that could be optimized through caching or moving to high-performance storage. We are expanding the prefix analytics in S3 Storage Lens to enable analyzing billions of prefixes per bucket, whereas previously metrics were limited to the largest prefixes that met minimum size and depth thresholds. This gives you visibility into storage usage and activity across all your prefixes. Finally, we are making it possible to export metrics directly to managed S3 Tables, making them immediately available for querying with AWS analytics services like Amazon QuickSight and enabling you to join this data with other AWS service data for deeper insights. To get started, enable performance metrics or expanded prefixes in your S3 Storage Lens advanced metrics dashboard configuration. These capabilities are available in all AWS Regions, except for AWS China Regions and AWS GovCloud (US) Regions. You can enable metrics export to managed S3 Tables in both free and advanced dashboard configurations in AWS Regions where S3 Tables are available. To learn more, visit the S3 Storage Lens overview page, documentation, S3 pricing page, and read the AWS News Blog.

Amazon RDS for SQL Server launches optimize CPU with support for M7i and R7i instance families, which reduce prices by up to 55% compared to equivalent previous generation instances. Optimize CPU optimizes Simultaneous Multi-threading (SMT) configuration to reduce commercial software charges. Customers can lower cost by upgrading to M7i and R7i instances from similar 6th generation instances. Furthermore, for memory or IO intensive database workloads, customers can get additional cost reduction by fine tuning optimize CPU configuration. RDS for SQL Server price for database instance hours consumed is inclusive of Microsoft Windows and Microsoft SQL Server software charges. Optimize CPU disables SMT for instances with 2 or more physical CPU cores. This reduces the number of vCPUs, and the corresponding commercial software charges by 50% while providing the same number of physical CPU cores, and near equivalent performance. The most significant savings are available on 2Xlarge and higher instances, and instances that use Multi-AZ deployment, where RDS optimizes to reduce SQL Server software charges for only a single active node for most usage. For workloads that are memory or IO intensive, customers can fine tune the number of active physical CPU cores for further savings. RDS for SQL Server supports M7i and R7i instances in all AWS Regions. With unbundled instance pricing, database costs are calculated with separate charges for third party licensing fees per vCPU hour, and third party licensing fees are not eligible towards your organization’s discounts with AWS. You can view Microsoft Windows and SQL Server charges associated with your usage on AWS Billing and Cost Management, and in monthly bills. For more details, visit RDS for SQL Server pricing, Amazon RDS User Guide and AWS News Blog.

AWS announces Amazon EC2 X8aedz, next generation memory optimized instances, powered by 5th Gen AMD EPYC processors (formerly code named Turin). These instances offer the highest maximum CPU frequency, 5GHz in the cloud. They deliver up to 2x higher compute performance compared to previous generation X2iezn instances. X8aedz instances are built using the latest sixth generation AWS Nitro Cards and are ideal for electronic design automation (EDA) workloads such as physical layout and physical verification jobs, and relational databases that benefit from high single-threaded processor performance and a large memory footprint. The combination of 5 GHz processors and local NVMe storage enables faster processing of memory-intensive backend EDA workloads such as floor planning, logic placement, clock tree synthesis (CTS), routing, and power/signal integrity analysis. X8aedz instances feature a 32:1 ratio of memory to vCPU and are available in 8 sizes ranging from 2 to 96 vCPUs with 64 to 3,072 GiB of memory, including two bare metal variants, and up to 8 TB of local NVMe SSD storage. X8aedz instances are now available in US West (Oregon) and Asia Pacific (Tokyo) regions. Customers can purchase X8aedz instances via Savings Plans, On-Demand instances, and Spot instances. To get started, sign in to the AWS Management Console. For more information visit the Amazon EC2 X8aedz instance page or AWS news blog.

Amazon S3 Tables now support automatic replication of Apache Iceberg tables across AWS Regions and accounts. This new capability replicates your complete table structure, including all snapshots and metadata to reduce query latency and improve data accessibility for global analytics workloads. S3 Tables replication automatically creates read-only replica tables in your destination table buckets, backfills them with the latest state of the source table, and continuously monitors for new updates to keep replicas in sync. Replica tables can be configured with independent snapshot retention policies and encryption keys from source tables to meet compliance and data protection requirements. You can query replica tables using Amazon SageMaker Unified Studio or any Iceberg-compatible engine including Amazon Athena, Amazon Redshift, Apache Spark, and DuckDB. S3 Tables replication is now available in all AWS Regions where S3 Tables are supported. For pricing details, visit the Amazon S3 pricing page. To learn more about S3 Tables, visit the product page, documentation, and read the AWS News Blog.

Amazon CloudWatch now provides new data management and analytics capabilities that allow you to unify operational, security, and compliance data across your AWS environment and third-party sources. DevOps teams, security analysts, and compliance officers can now access all their data in a single place, eliminating the need to maintain multiple separate data stores and complex (extract-transform-load) ETL pipelines. CloudWatch now offers greater flexibility in where and how customers gain insights into this data, both natively in CloudWatch or with any Apache Iceberg-compatible tool. With the unified data store enhancements, customers can now easily collect and aggregate logs across AWS accounts and regions aligned to geographic boundaries, business units, or persona-specific requirements. With AWS Organization-wide enablement for AWS sources such as AWS CloudTrail, Amazon VPC, and Amazon WAF, and managed collectors for third party sources such as Crowdstrike, Okta, Palo Alto Networks, CloudWatch makes it easy to bring more of your logs together. Customers can use pipelines to transform and enrich their logs to standard formats such as Open Cybersecurity Schema Framework (OCSF) for security analytics, and define facets to accelerate insights on their data. Customers can make their data available in managed Amazon S3 Tables at no additional storage charge, enabling teams to query data in Amazon SageMaker Unified Studio, Amazon Quick Suite, Amazon Athena, Amazon Redshift, or any Apache Iceberg-compatible analytics tool. To get started, visit the Ingestion page in the CloudWatch console and add one or more data sources. To learn more about Amazon CloudWatch unified data store, visit the product page, pricing page, and documentation. For Regional availability, visit the AWS Builder Center.

We are excited to announce Amazon Nova 2 Omni, an all-in-one model for multimodal reasoning and image generation. It is the industry’s first reasoning model that supports text, images, video, and speech inputs while generating both text and image outputs. It enables multimodal understanding, image generation and editing using natural language, and speech transcription. Unlike traditional approaches that often force organizations to stitch together various specialized models, each supporting different input and output types, Nova 2 Omni eliminates the complexity of managing multiple AI models. This helps to accelerate application development while reducing complexity and costs, enabling developers to tackle diverse tasks from marketing content creation and customer support call transcription to video analysis and documentation with visual aids. The model supports a 1M token context window, 200+ languages for text processing and 10 languages for speech input. It can generate and edits high-quality images using natural language, enabling character consistency, text rendering within image as well as object and background modification. Nova 2 Omni delivers superior speech understanding with native reasoning to transcribe, translate and summarize multi-speaker conversations. And with flexible reasoning controls for depth and budget, developers can ensure optimal performance, accuracy, and cost management across different use cases. Nova 2 Omni is in preview with early access available to all Nova Forge customers. Please reach out to your AWS account team for access. To learn more about Amazon Nova 2 Omni read the user guide. 

Amazon S3 Vectors, the first cloud object storage with native support to store and query vectors, is now generally available. S3 Vectors delivers purpose-built, cost-optimized vector storage for AI agents, inference, Retrieval Augmented Generation (RAG), and semantic search at billion-vector scale. S3 Vectors is designed to provide the same elasticity, durability, and availability as Amazon S3 and reduces the total costs to upload, store, and query vectors by up to 90%. With general availability, you can store and query up to two billion vectors per index and elastically scale to 10,000 vector indexes per vector bucket. Infrequent queries continue to return results in under one second, with more frequent queries now resulting in latencies around 100 milliseconds or less. Your application can achieve write throughput of 1,000 vectors per second when streaming single-vector updates into your indexes, retrieve up to 100 search results per query, and store up to 50 metadata keys alongside each vector for fine-grained filtering in your queries. With S3 Vectors you get a new bucket type—a vector bucket—that is optimized for durable, low-cost vector storage. Within vector buckets, you organize your vector data with vector indexes and get a dedicated set of APIs to store, access, and query vectors without provisioning any infrastructure. By default, S3 Vectors encrypts all vector data in a vector bucket with server-side encryption using S3-managed keys (SSE-S3) or optionally, you can use AWS Key Management Service (SSE-KMS) to set a default customer-managed key to encrypt all new vector indexes in the vector bucket. You can now also set a dedicated customer-managed key per vector index, helping you build scalable multi-tenant applications and meet regulatory and governance requirements. You can also tag vector buckets and indexes for attribute-based access control (ABAC) as well as to track and organize costs using AWS Billing and Cost Management. S3 Vectors integrates with Amazon Bedrock Knowledge Bases to reduce the cost of using large vector datasets for RAG. When creating a Knowledge Base in Amazon Bedrock or Amazon SageMaker Unified Studio, you can choose an existing Amazon S3 vector index or create a new one using the Quick Create workflow. With Amazon OpenSearch Service, you can optimize costs for hybrid search workloads by configuring OpenSearch to automatically manage vector storage in S3. S3 Vectors is now generally available in 14 AWS Regions, expanding from 5 Regions in preview. To learn more, visit the product page, S3 pricing page, documentation, and AWS News blog.

AWS announces the general availability of new compute-optimized Amazon EC2 C8a instances. C8a instances are powered by 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, delivering up to 30% higher performance and up to 19% better price-performance compared to C7a instances. C8a instances deliver 33% more memory bandwidth compared to C7a instances, making these instances ideal for latency sensitive workloads. Compared to Amazon EC2 C7a instances, they are up to 57% faster for GroovyJVM allowing better response times for Java-based applications. C8a instances offer 12 sizes including 2 bare metal sizes. This range of instance sizes allows customers to precisely match their workload requirements. C8a instances are built on AWS Nitro System and are ideal for high performance, compute-intensive workloads such as batch processing, distributed analytics, high performance computing (HPC), ad serving, highly-scalable multiplayer gaming, and video encoding. C8a instances are available in the following AWS Regions: US East (N. Virginia), US East (Ohio), and US West (Oregon) regions. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 C8a instance page.

We're excited to launch AWS DevOps Agent in preview, a frontier agent that resolves and proactively prevents incidents, continuously improving reliability and performance of applications in AWS, multicloud, and hybrid environments. AWS DevOps Agent investigates incidents and identifies operational improvements as an experienced DevOps engineer would: by learning your resources and their relationships, working with your observability tools, runbooks, code repositories, and CI/CD pipelines, and correlating telemetry, code, and deployment data across all of them to understand the relationships between your application resources. AWS DevOps Agent autonomously triages incidents and guides teams to rapid resolution to reduce Mean Time to Resolution (MTTR). AWS DevOps Agent begins investigating the moment an alert comes in, whether at 2 AM or during peak hours, to quickly restore your application to optimal performance. It analyzes patterns across historical incidents to provide actionable recommendations that strengthen key areas including observability, infrastructure optimization, and deployment pipeline enhancement. AWS DevOps Agent helps access the untapped insights in your operational data and tools without changing your workflows. AWS DevOps Agent is available at no additional cost during preview in the US East (N. Virginia) Region. To learn more, read the AWS News Blog and see getting started.

AWS launches the preview of Amazon EC2 C8ine instances, powered by custom sixth-generation Intel Xeon Scalable processors (Granite Rapids) and the latest AWS Nitro v6 card. These instances are designed specifically for dataplane packet processing workloads. Amazon EC2 C8ine instance configurations can deliver up to 2.5 times higher packet performance per vCPU versus prior generation C6in instances. They can offer up to 2x higher network bandwidth through internet gateways and up to 3x more Elastic Network Interface (ENI) compared to existing C6in network optimized instances. They are ideal for packet processing workloads requiring high performance at small packet sizes. These workloads include security virtual appliances, firewalls, load balancers, DDoS protection systems, and Telco 5G UPF applications. These instances are available for preview upon request through your AWS account team. Connect with your account representatives to signup.

Amazon S3 Batch Operations now completes jobs up to 10x faster at a scale of up to 20 billion objects in a job, helping you accelerate large-scale storage operations. With S3 Batch Operations, you can perform operations at scale such as copying objects between staging and production buckets, tagging objects for S3 Lifecycle management, or computing object checksums to verify the content of stored datasets. S3 Batch Operations now pre-processes objects, executes jobs, and generates completion reports up to 10x faster for jobs processing millions of objects with no additional configuration or cost. To get started, create a job in the AWS Management Console and specify operation type as well as filters like bucket, prefix, or creation date. S3 automatically generates the object list, creates an AWS Identity and Access Management (IAM) role with permission policies as needed, then initiates the job. S3 Batch Operations performance improvements are available in all AWS Regions, except for AWS China Regions and AWS GovCloud (US) Regions. For pricing information, please visit the Management & Insights tab of the Amazon S3 pricing page. To learn more about S3 Batch Operations, visit the overview page and documentation.

AWS Support announces a transformation of its Support portfolio, simplified into three intelligent, experience-driven plans: Business Support+, Enterprise Support, and Unified Operations. Each plan combines the speed and precision of AI with the expertise of AWS engineers. Each higher plan builds on the previous one, adding faster response times, proactive guidance, and smarter operations. The result: reduced engineering burden, stronger reliability and resiliency, and streamlined cloud operations. Business Support+ delivers 24/7 AI-powered assistance that understands your context, with direct engagement to AWS experts for critical issues within 30 minutes—twice as fast as current plans. Enterprise Support expands on this with designated Technical Account Managers (TAMs) who blend generative AI insights with human judgment to provide strategic operational guidance across resiliency, cost, and efficiency. It also includes AWS Security Incident Response at no additional cost, which customers can activate to automate security alert investigation and triage. Unified Operations, the top plan, is designed for mission-critical workloads—offering a global team of designated experts who deliver architecture reviews, guided testing, proactive optimization, and five-minute context-specific response times for critical incidents. Customers using AWS DevOps Agent (preview) can engage with AWS Support with one-click from an investigation when needed, giving AWS experts immediate context for faster resolution. AWS DevOps Agent is a frontier agent that resolves and proactively prevents incidents, continuously improving reliability and performance of applications in AWS, multicloud, and hybrid environments. Business Support+, Enterprise Support, and Unified Operations are available in all commercial AWS Regions. Existing customers can continue with their current plans or explore the new offerings for enhanced performance and efficiency. To see how AWS blends AI intelligence and human expertise to transform your cloud operations, visit the AWS Support product page.

You can now build billion-scale vector databases in under an hour on Amazon OpenSearch Service with GPU-acceleration, and auto-optimize vector indexes for optimal trade-offs between search quality, speed and cost. Previously, large-scale vector indexes took days to build, and optimizing them required experts to spend weeks of manual tuning. The time, cost and effort weighed down innovation velocity, and customers forwent cost and performance optimizations. You can now run serverless, auto-optimize jobs to generate optimization recommendations. You simply specify search latency and recall requirements, and these jobs will evaluate index configurations (k-NN algorithms, quantization, and engine settings) automatically. Then, you can use vector GPU-acceleration to build an optimized index up to 10X faster at a quarter of the indexing cost. Serverless GPUs dynamically activate and accelerate your domain or collection, so you’re only billed when you benefit from speed boosts—all done without you managing GPU instances. These capabilities help you scale AI applications including semantic search, recommendation engines, and agentic systems more efficiently. By simplifying and accelerating the time to build large-scale, optimized vector databases, your team will be empowered to innovate faster. Vector GPU-acceleration is available for vector collections and OpenSearch 3.1+ domains in US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney), Europe (Ireland), and Asia Pacific (Tokyo) Regions. Vector auto-optimize is available for vector collections and OpenSearch 2.17+ domains in US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt) and Europe (Ireland) Regions. Learn more.

Today AWS announces Amazon Nova 2, our next generation of general models that deliver reasoning capabilities with industry-leading price performance. The new models available today in Amazon Bedrock are: • Amazon Nova 2 Lite, a fast, cost-effective reasoning model for everyday workloads. • Amazon Nova 2 Pro (Preview), our most intelligent model for highly complex, multistep tasks. Amazon Nova 2 Lite and Amazon Nova 2 Pro (Preview) offer significant advancements over our previous generation models. These models support extended thinking with step-by-step reasoning and task decomposition and include three thinking intensity levels—low, medium, and high—giving developers control over the balance of speed, intelligence, and cost. The models also offer built-in tools such as code interpreter and web grounding, support remote MCP tools, and provide a one-million-token context window for richer interactions. Nova 2 Lite can be used for a broad range of your everyday tasks. It offers the best combination of price, performance, and speed. Early customers are using Nova 2 Lite for customer service chatbots, document processing, and business process automation. Nova 2 Lite can be customized using supervised fine-tuning (SFT) on Amazon Bedrock and Amazon SageMaker, and full fine-tuning is available on Amazon SageMaker. Amazon Nova 2 Pro (Preview) can be used for highly complex agentic tasks such as multi-document analysis, video reasoning, and software migrations. Amazon Nova 2 Lite and Nova 2 Pro (Preview) is now available in Amazon Bedrock via global cross region inference in multiple locations. Nova 2 Pro is in preview with early access available to all Amazon Nova Forge customers. If interested, reach out to your AWS account team regarding access. Learn more at the AWS News Blog, Amazon Nova models product page, and Amazon Nova user guide.

Amazon S3 increased the maximum object size to 50 TB, a 10x increase from the previous 5 TB limit. This simplifies the processing of large objects such as high-resolution videos, seismic data files, AI training datasets and more. You can store 50 TB objects in all S3 storage classes and use them with all S3 features. Optimize upload and download performance for your large objects by using the latest AWS Common Runtime (CRT) and S3 Transfer Manager in the AWS SDK. You can apply S3's storage management capabilities to these objects. For example, use S3 Lifecycle to automatically archive infrequently accessed objects to S3 Glacier storage classes, or use S3 Replication to copy objects across AWS accounts or Regions. Amazon S3 supports objects up to 50 TB in all AWS Regions. To learn more about working with large objects, visit the S3 User Guide.