.Announcements of AWS re:Invent 2023

Nov 9th 2023-2 min read

The yearly re:Invent conference, a highlight for every AWS enthusiast, is now once again being hosted as an on-site event like last year.

The last five consecutive years (2022, 2021, 2020, 2019 and 2018) we have kept you up to date with the latest developments from the conference, and we will continue this tradition in the current year.

Revisit this page during re:Invent to find the latest service updates and feature announcements.

AWS re:Invent Blog - AWS Twitch - AWS Youtube Channel - AWS on Linkedin

🎥 Here is a nice video with some insides

(This post was last updated:
Wednesday, 30-Nov-2023 15:45:00 UTC-8: Pacific Standard Time (PST))

Event information

Where: Las Vegas, NV
When: NOV. 27 – DEC. 1, 2023


FeatureServiceDescriptionPreview / Region availability
GlueAWS introduces a preview of a new feature in AWS Glue Data Quality that leverages machine learning to detect statistical anomalies and unusual patterns, enhancing data quality. The feature provides insights into data quality issues, scores, and recommendations for rules to monitor anomalies, all without requiring any code.
CleanRoomsAWS Clean Rooms Differential Privacy, now in preview, safeguards user privacy using intuitive controls. It's part of AWS Clean Rooms, requiring no prior differential privacy expertise. It obscures individual data contributions, enabling secure collaboration for generating diverse insights through SQL queries, applicable to fields like advertising, investment, and clinical research.Preview
CleanRooms Introducing AWS Clean Rooms ML (preview), a feature of AWS Clean Rooms enabling collaborative machine learning (ML) model application on collective data without raw data sharing. The first model, focused on creating lookalike segments for marketing, allows custom training and secure collaboration to generate expanded sets of similar records while safeguarding sensitive data.Preview
OpenSearch The new OR1 instances for Amazon OpenSearch Service allow the creation of clusters using Amazon S3 for primary storage. Offering a 30% price/performance boost over existing instances, these clusters ensure eleven nines of data durability and zero-time Recovery Point Objective. Users can perform tasks like interactive log analytics and real-time application monitoring with the ability to ingest, store, index, and access vast amounts of data.

Application Integration

FeatureServiceDescriptionPreview / Region availability
StepfunctionsAWS Step Functions now supports HTTPS endpoints, making it easier to integrate third-party APIs and external services into your workflows. This simplifies the process of connecting with services like Stripe for payments, GitHub for code collaboration, and Salesforce for sales and marketing, eliminating the need for a separate AWS Lambda function to handle authentication and errors.
EventBridgeAmazon EventBridge has expanded its support for partner integrations, now including Adobe I/O events and Stripe. This allows users to seamlessly route events from these platforms to over 20 AWS services. This enhancement simplifies the development of event-driven architectures for tasks like handling payments, invoices, and ordering. By utilizing these integrations, developers can improve agility, reduce time spent on integration code, and accelerate feature development by combining SaaS capabilities from Adobe and Stripe with AWS services.
StepfunctionsAWS integrates AWS Step Functions Workflow Studio into AWS Application Composer, offering a unified visual Infrastructure as Code (IaC) builder. This enables a seamless transition between workflow authoring with Workflow Studio and resource definition with Application Composer. Users can create and manage resources at any development stage, visualizing the complete application in Application Composer and diving into workflow details with Workflow Studio, all within a single interface.
SQS Amazon SQS enhances FIFO queues with increased throughput, supporting up to 70,000 transactions per second in selected AWS Regions, allowing batching for up to 700,000 messages per second. Additionally, Dead Letter Queue (DLQ) redrive support is introduced, managing unconsumed messages after a specified number of retries, similar to standard queues.

Artificial Intelligence

FeatureServiceDescriptionPreview / Region availability
Amazon TranscribeAmazon Transcribe Call Analytics introduces generative AI-powered call summarization in preview. Utilizing Amazon Bedrock, this feature automatically summarizes customer service calls, aiming to enhance customer experience and boost agent and supervisor productivity. With a single API call, businesses can extract transcripts, rich insights, and summaries, allowing for sentiment analysis, trend identification, and policy compliance assessment in customer conversations.Preview
Amazon BedrockAWS Step Functions introduces two new optimized integrations with Amazon Bedrock. Step Functions is a visual workflow service designed to assist developers in building distributed applications, automating processes, orchestrating microservices, and creating data and machine learning (ML) pipelines.
DataZone Amazon introduces a preview of an automation feature for Amazon DataZone, backed by generative artificial intelligence. This feature, powered by Amazon Bedrock's large language models, automates the labor-intensive process of data cataloging. It generates detailed descriptions of data assets, schemas, and suggests analytical use cases, significantly reducing the time needed to provide context for organizational data with a single click.Preview
Amazon ConnectIn contact centers, agents play a crucial role in building customer trust and loyalty. Their assistance is vital for guiding through complex decisions and providing fast, accurate solutions. Effective agent interactions are key to preventing customer frustration.
Amazon Q Amazon Q in Amazon QuickSight is now available for preview. Business users can leverage Generative BI capabilities, turning insights into impactful stories, accessing executive summaries of dashboards, and utilizing a reimagined Q&A experience to confidently address unanswered data questions.Preview
Amazon Q Amazon introduces Amazon Q, a generative AI-powered assistant tailored for business. Amazon Q facilitates conversations, problem-solving, content generation, and gaining insights by connecting to company information repositories, code, data, and enterprise systems. It aims to provide immediate, relevant information and advice, streamlining tasks, accelerating decision-making, problem-solving, and fostering creativity and innovation at work.Preview
Amazon Q Amazon introduces Amazon Q Code Transformation, in preview, to simplify upgrading and modernizing existing application code. This capability leverages Amazon Q, a generative AI-powered assistant designed for business, to streamline the process. Developers can benefit from reduced effort in managing upgrades, avoiding the need to relearn intricacies, and balancing focus between new features and maintenance work.Preview
Amazon Q Amazon CodeCatalyst introduces new generative AI capabilities in preview, leveraging Amazon Q to accelerate software delivery. The feature development capability helps speed up tasks such as adding comments, refining issue descriptions, generating small classes and unit tests, and updating workflows, freeing developers from tedious and undifferentiated work.Preview
Amazon Q Amazon Q, a generative AI-powered assistant, is now in preview. Tailored for work, it supports developers and IT professionals in building applications on AWS, researching best practices, resolving errors, and coding new features. Amazon Q Code Transformation can perform Java application upgrades, from version 8 and 11 to version 17.Preview
BedrockKnowledge Bases for Amazon Bedrock is now generally available. This feature allows secure connections between foundation models (FMs) in Amazon Bedrock and company data for Retrieval Augmented Generation (RAG). By accessing additional data, the model can generate more relevant and accurate responses without frequent retraining. All retrieved information from knowledge bases includes source attribution for transparency and minimizing hallucinations.
Bedrock Amazon Bedrock now allows private and secure customization of foundation models (FMs) with your own data. This enables building applications tailored to your domain, organization, and use case. Fine-tuning increases model accuracy with task-specific labeled training datasets, while continued pre-training using your unlabeled data enhances domain specificity, knowledge, and adaptability in a secure and managed environment with customer-managed keys.
Bedrock Agents for Amazon Bedrock accelerate generative AI application development by orchestrating multistep tasks. Utilizing the reasoning capability of foundation models (FMs), agents break down user-requested tasks, create an orchestration plan based on developer-provided instructions, and execute the plan by invoking company APIs. They access knowledge bases using Retrieval Augmented Generation (RAG) to provide a final response to end-users, enabling sophisticated AI interactions.
Bedrock Guardrails for Amazon Bedrock (preview) is introduced to promote safe interactions in generative AI applications. As part of a responsible AI strategy, users can implement customized safeguards aligning with company policies and principles. Guardrails assist in defining denied topics and content filters, enhancing control over undesirable and harmful content in user interactions with applications built on foundation models (FMs). This supports AWS's commitment to responsible AI development.Preview
Supply Chain AWS introduced four enhancements to AWS Supply Chain, including Supply Planning for efficient inventory management, N-Tier Visibility for better communication with suppliers, Sustainability for centralizing sustainability data, and Amazon Q for AI-driven conversational support in supply chain analysis and decision-making. These features aim to enhance planning, collaboration, sustainability tracking, and decision support in the supply chain.
BedrockAmazon Bedrock now offers Model Evaluation in preview, allowing you to assess, compare, and choose the best foundation models (FMs) for your use case. It provides automatic evaluation with predefined metrics like accuracy and robustness, as well as the option for human evaluation for subjective or custom metrics such as friendliness and style. This tool is crucial at all development stages, enabling experimentation, faster iteration with automatic evaluations, and incorporating human reviews for quality assurance during launches.
BedrockStability AI’s Stable Diffusion XL 1.0 (SDXL 1.0) foundation model is now generally available on-demand in Amazon Bedrock. SDXL 1.0 is the most advanced development in the Stable Diffusion text-to-image suite of models launched by Stability AI. The model generates images of high quality in virtually any art style and it excels at photorealism. Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models from leading AI companies, like Stability AI, along with a broad set of capabilities that provide you with the easiest way to build and scale generative AI applications with foundation models.
SageMaker Amazon SageMaker Clarify has introduced a new capability enabling customers to evaluate foundation models (FMs) swiftly. AWS users can now compare and select FMs based on metrics like accuracy, robustness, bias, and toxicity within minutes. This addresses the challenge customers face in choosing the most suitable FM for their generative AI applications, as the traditional process involves spending days on benchmark identification, tool setup, and assessments, often resulting in complex and hard-to-interpret results.Preview
SageMaker Amazon SageMaker HyperPod is now generally available, facilitating the training of foundation models with thousands of accelerators up to 40% faster. This offering provides a resilient training environment, reducing the operational burden of managing large-scale training clusters. SageMaker HyperPod ensures uninterrupted training for weeks or months, eliminating concerns related to hardware failures for machine learning practitioners.
SageMakerLaunch fully managed JupyterLab in seconds with pre-configured SageMaker Distribution, featuring popular ML libraries. Access the latest JupyterLab 4 version, generative AI-powered coding companions like Amazon Code Whisperer, and scale compute resources with ease. Persist packages across instances by creating custom conda environments and bring custom-built images for personalized JupyterLab and ML libraries.

Billing & Account Management

FeatureServiceDescriptionPreview / Region availability
AWS Free TierAWS introduces the AWS Free Tier API, allowing users to check their usage of the AWS Free Tier directly through the AWS CLI or integrate it into applications using AWS SDKs. The AWS Free Tier provides free usage of AWS services up to specified limits, with always free offers, 12 months free offers, and short-term trials available. Users need to monitor their usage to switch to pay-as-you-go pricing once they approach the free tier limits.
Cost Optimization HubAWS introduces Cost Optimization Hub, a feature in AWS Billing and Cost Management, facilitating the identification and quantification of savings for AWS cost optimization recommendations. This tool allows users to interactively query recommendations like idle resource detection and rightsizing across multiple AWS Regions and accounts. It provides insights into potential savings and allows for easy comparison and prioritization of recommendations based on cost savings.


FeatureServiceDescriptionPreview / Region availability
EKSAmazon introduces a new capability called Amazon Managed Service for Prometheus collector. This feature automatically and agentlessly discovers and collects Prometheus metrics from Amazon Elastic Kubernetes Service (Amazon EKS). The collector includes a scraper that retrieves metrics from Amazon EKS applications and infrastructure without the need to run collectors in the cluster.
EKS In 2019, IAM roles for service accounts (IRSA) were introduced, allowing association of an IAM role with a Kubernetes service account. This facilitated implementing the principle of least privilege for pods, granting only necessary permissions. Now, Amazon EKS Pod Identity makes it simpler to configure and automate AWS permissions for Kubernetes identities. Cluster administrators no longer need to switch between Amazon EKS and IAM services for authenticating applications to AWS resources. This streamlines the process of granting AWS permissions to Kubernetes entities.
LambdaAWS Lambda now scales up to 12 times faster, with synchronous invocation scaling by 1,000 concurrent executions every 10 seconds. Each function within an account scales independently, and these enhancements are cost-free, requiring no configuration changes in existing functions.
EKSThe new Mountpoint for Amazon S3 Container Storage Interface (CSI) driver enables Kubernetes applications to access S3 objects through a file system interface, achieving high throughput without application changes. It presents an S3 bucket as a volume accessible by containers in Amazon EKS and self-managed Kubernetes clusters, benefiting distributed machine learning training jobs for accelerated data reading from Amazon S3.


FeatureServiceDescriptionPreview / Region availability
ALBMutual TLS for ALB offers two validation options for X.509 client certificates. In passthrough mode, ALB sends the full certificate chain to the target via HTTP headers for application-level authentication. In verify mode, ALB handles X.509 client certificate authentication during TLS connection negotiation.
EFSAmazon introduces EFS Archive, a new storage class for Amazon Elastic File System (Amazon EFS) tailored for long-lived data rarely accessed. With this addition, Amazon EFS now offers three Regional storage classes: EFS Standard for active data, EFS Infrequent Access (EFS IA) for data accessed infrequently, and EFS Archive for cost-optimized, long-lived data accessed a few times a year or less. All storage classes provide high throughput, IOPS performance, and eleven nines of durability.


FeatureServiceDescriptionPreview / Region availability
RDSAmazon Cloud Development Kit (CDK) now supports building scalable, secure GraphQL interfaces over relational databases. Using the AWS Amplify GraphQL API CDK construct, you can securely store database credentials in AWS Systems Manager Parameter Store and author GraphQL APIs to execute SQL statements. This capability is compatible with MySQL and PostgreSQL databases on Amazon RDS or external hosts.
RDSAWS has introduced Amazon Aurora Limitless Database, allowing users to scale Aurora clusters to handle millions of write transactions per second and manage petabytes of data. This feature enables scaling relational database workloads on Aurora beyond the constraints of a single writer instance, eliminating the need for custom application logic or the management of multiple databases.Preview
RDS IBM and AWS collaborate to offer Amazon RDS for Db2, a fully managed Db2 database engine on AWS. Db2, an enterprise-grade RDBMS by IBM, provides robust features, security, scalability, and diverse data type support. Originating in the 1970s, Db2 has evolved, commercially available since 1983, and now powers numerous business-critical applications across platforms.
ElastiCacheAmazon introduces Amazon ElastiCache Serverless, a rapid and scalable caching solution compatible with Redis and Memcached. This serverless option enables quick cache creation, instant capacity scaling based on application traffic, and efficient operation for demanding workloads without the need for capacity planning or caching expertise. ElastiCache Serverless monitors resource utilization and ensures high availability with automatic data replication across multiple Availability Zones, offering up to a 99.99% SLA for all workloads, saving both time and costs.
DynamoDB Amazon DynamoDB now offers general availability for zero-ETL integration with Amazon OpenSearch Service. This enables seamless searching of DynamoDB data without custom code, reducing operational burden and costs associated with data pipeline management, allowing users to focus on their applications.
RDS Customers seeking data-driven insights for competitive advantage often build solutions combining a database, data warehouse, and ETL pipeline. Extract, transform, load (ETL) is crucial for data engineers to merge information from diverse sources, enabling analytics on operational data to understand core business drivers for sales growth, cost reduction, and business optimization.
DynamoDB Amazon DynamoDB now supports zero-ETL integration with Amazon Redshift, allowing customers to run high-performance analytics on DynamoDB data without impacting production workloads. Data written into DynamoDB is seamlessly available in Amazon Redshift, eliminating the need for complex ETL pipelines.
RDS Amazon Aurora zero-ETL integration with Amazon Redshift allows near real-time analytics and machine learning on petabytes of transactional data from Amazon Aurora. Aurora PostgreSQL-Compatible Edition database clusters can now be used (in public preview) as a source, providing seamless data availability in Amazon Redshift within seconds of being written into Aurora, eliminating the need for complex ETL pipelines.Preview

Developer Tools

FeatureServiceDescriptionPreview / Region availability
CodeCatalystAmazon introduces the Amazon CodeCatalyst enterprise tier, priced at $20/user per month, offering custom blueprints and project lifecycle management. This tier includes 1,500 compute minutes, 160 Dev Environment hours, and 64GB of Dev Environment storage per paying user. Custom blueprints enable defining best practices for application code, workflows, and infrastructure, which can be published and applied to projects within CodeCatalyst.


AWS launches re:Post Private, a fully managed knowledge service designed to accelerate cloud adoption, enhance productivity, and foster innovation. It offers curated technical content and training materials from AWS tailored for an organization's use cases. Access is limited to members of the organization and their AWS account team, providing private discussion and collaboration forums. It serves as a dedicated, organization-specific version of AWS re:Post.
The AWS SDK for Rust offers an idiomatic, type-safe API, leveraging Rust's performance, reliability, and productivity. Supporting modern Rust features, it provides access to 300+ AWS services with individual crates. The SDK is extensible, modular, and engineered for speed, enabling quick data transfer to and from services like Amazon S3, Amazon EC2, and Amazon DynamoDB.
The AWS SDK for Kotlin provides an idiomatic Kotlin experience with DSL builders and supports asynchronous AWS service calls using coroutines. Developers can target JVM or Android API Level 24+, with plans for Kotlin/Native support in future releases. The SDK features DSL builders, coroutines support, pagination, waiters, pluggable HTTP layer, and more.
Generative AI's rapid growth presents both promise and challenges, including bias, explainability, hallucination, and toxicity, especially in foundation models (FMs). AWS is dedicated to responsible development of generative AI, prioritizing education, science, and customer integration. The approach emphasizes a people-centric perspective, ensuring responsible AI practices across the entire AI lifecycle.
Amazon announces the general availability of myApplications, offering enhanced capabilities for starting, operating, and scaling applications on AWS. Accessible in the AWS Management Console, myApplications simplifies application management, allowing users to monitor cost, health, security, and performance seamlessly. The new Create application wizard facilitates easy application creation and resource connection in one console view. The applications created are automatically integrated into myApplications for streamlined monitoring and action-taking.
Werner Vogels - Simple laws for building cost-aware, sustainable, and modern architectures.

Management & Governance

FeatureServiceDescriptionPreview / Region availability
CloudWatchAmazon CloudWatch now supports natural language queries for Logs and Metrics Insights. Using generative AI, users can describe their data insights in English, and CloudWatch will automatically generate the corresponding Logs or Metrics Insights queries. This feature offers three key capabilities: generating new queries from descriptions or questions, providing query explanations for learning advanced features, and refining existing queries through guided iterations.Preview
CloudWatchAmazon CloudWatch allows you to centralize metrics from hybrid, multicloud, and on-premises sources, enabling consistent processing. This feature enables querying, visualization, and alerting for all metrics, regardless of their origin. It provides a unified view, facilitating the identification of trends and issues across diverse parts of your infrastructure.
CloudWatchAmazon CloudWatch introduces features to simplify log data analysis. Using advanced machine learning, it automatically identifies and clusters patterns in log records, extracts important content and trends, and notifies users of anomalies. This aims to streamline the process of finding operational or business insights within log data, eliminating the need for manual filtering and review.
CloudWatchAmazon CloudWatch Logs introduces a new log class called Infrequent Access, designed to provide cost-effective storage and capabilities for infrequently accessed logs. This allows customers to consolidate their logs in one place with reduced costs.
CloudFormationAWS CloudFormation, an Infrastructure as Code (IaC) service, now supports Git sync, allowing automatic deployment triggers when a tracked Git repository is updated. This integration streamlines the CloudFormation development cycle, enabling developers to sync seamlessly with their Git workflows and reducing time lost to context switching. The feature is compatible with GitHub, GitHub Enterprise, GitLab, and Bitbucket.
CloudWatchAmazon CloudWatch Application Signals helps you automatically instrument applications based on best practices for application performance. There is no manual effort, no custom code, and no custom dashboards. You get a pre-built, standardized dashboard showing the most important metrics, such as volume of requests, availability, latency, and more, for the performance of your applications. In addition, you can define Service Level Objectives (SLOs) on your applications to monitor specific operations that matter most to your business. An example of an SLO could be to set a goal that a webpage should render within 2000 ms 99.9 percent of the time in a rolling 28-day interval.Preview

Security, Identity, & Compliance

FeatureServiceDescriptionPreview / Region availability
IAM Access AnalyzerAWS introduces Unused Access Analyzer, a new analyzer that continually monitors roles and users for permissions granted but not utilized. It offers a dashboard for central security teams to identify accounts requiring a review of unused permissions, roles, and IAM users. Additionally, Custom Policy Checks enable validation of newly authored policies to prevent unintended permissions. This feature allows tighter control over IAM policies and facilitates automated policy reviews in CI/CD pipelines and custom policy tools, expediting the transition of AWS applications from development to production.
Security HubAWS introduced customization features for AWS Security Hub controls, enabling users to personalize security posture monitoring in the AWS environment. Security Hub, a cloud security posture management (CSPM) service, offers automated controls for monitoring cloud resources and ensuring secure configurations. The update allows security teams to tailor the monitoring of best practices to meet specific security requirements.
GuardDutyAmazon is introducing GuardDuty ECS Runtime Monitoring to identify potential security issues in Amazon ECS clusters running on AWS Fargate and Amazon EC2. GuardDuty utilizes machine learning, anomaly detection, network monitoring, and malicious file discovery against AWS data sources. When security threats are detected, GuardDuty creates findings and sends them to AWS Security Hub, Amazon EventBridge, and Amazon Detective. These integrations streamline monitoring, trigger automated responses, and facilitate security investigations for AWS and
Detective Detective now offers automated investigations for IAM, aiding in analyzing IAM objects for potential compromise using MITRE ATT&CK tactics. Additionally, generative AI enhances findings group summaries, providing natural language insights into security investigations. This feature simplifies the analysis of finding groups across various AWS data sources, making it quicker to investigate and understand unusual or suspicious activities. These capabilities are accessible through the Detective section of AWS Management Console or via a new API, allowing for automation and integration with other systems like AWS Security Hub or SIEM.
GuardDuty AWS has launched Amazon GuardDuty EC2 Runtime Monitoring, expanding threat detection for Amazon EC2 workloads. It provides visibility into on-host, OS-level activities and offers container-level context for detected threats, helping identify and respond to potential threats targeting compute resources within EC2 workloads. The extended capability covers instances or self-managed containers querying IP addresses linked to cryptocurrency activity or connecting to a Tor network. It ensures full runtime visibility across AWS, reducing the attack surface and mitigating risks for applications and workloads.Preview
IAM Identity Center AWS introduces IAM Identity Center application assignment APIs for automated management and audit of user and group access to AWS managed applications. This eliminates the need for manual assignments through the console, enabling scalable and efficient access control as organizations expand.
Control TowerAWS Control Tower now includes 65 purpose-built controls to assist in meeting digital sovereignty requirements. Digital sovereignty involves controlling digital assets, data location, flow, and access. AWS is committed to providing customers with control over their data, exemplified by the AWS Digital Sovereignty Pledge and initiatives like validating the AWS Nitro System and launching Dedicated Local Zones. The recent announcement includes plans for a new independent sovereign Region in Europe.
Control Tower AWS Control Tower now allows customers to programmatically set up and manage landing zones using APIs. Landing zones are well-architected, multi-account AWS environments following security and compliance best practices. AWS Control Tower automates landing zone setup with best-practice blueprints for identity, federated access, logging, and account structure. The landing zone APIs include AWS CloudFormation support, enabling customers to manage their landing zones with infrastructure as code (IaC).
InspectorAmazon Inspector now integrates with CI/CD tools like Jenkins and TeamCity for container image assessments. This allows developers to identify software vulnerabilities earlier in the development lifecycle. Assessment findings appear in the CI/CD tool’s dashboard, enabling automated responses to security issues. Simply install the Amazon Inspector plugin and add a scan step in the build pipeline. This feature is accessible across various hosting environments, ensuring a consistent security solution for developers.


FeatureServiceDescriptionPreview / Region availability
S3Users can enhance security in Amazon Simple Storage Service (Amazon S3) by applying the principle of least privilege, defining precise access based on applications, personas, groups, or organizational units. This practice minimizes the risk of unauthorized access, limiting potential damage in case of a security breach. Additionally, comprehensive auditability is crucial for tracking and analyzing user activities, ensuring compliance with regulatory requirements and facilitating the swift detection of anomalous behavior or security incidents.
AWS Backup Automated game day testing of critical resources is vital for readiness against ransomware or data loss. AWS Backup introduces restore testing, enabling automated testing across storage, compute, and databases. This helps ensure successful recovery and compliance with organizational and regulatory data governance requirements.
S3 Amazon introduces the S3 Express One Zone storage class, offering up to 10x better performance than S3 Standard. Tailored for frequently accessed data and demanding applications, it handles hundreds of thousands of requests per second with consistent single-digit millisecond latency. Objects are stored and replicated within a single AWS Availability Zone, enabling co-location of storage and compute resources for reduced latency.
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