On 7/30, OpenAI announced updated pricing for GPT-5.6 Terra and GPT-5.6 Luna. 

GPT-5.6 Terra is the balanced model for everyday production work, delivering GPT-5.5-level performance at lower cost. GPT-5.6 Luna is the fast, affordable model for high-volume inference tasks where latency and cost per token matter most. GPT-5.6 Sol pricing remains unchanged. Pricing on Amazon Bedrock matches OpenAI first-party rates, and usage counts toward your existing AWS commitments. 

GPT-5.6 Sol is available in US East (N. Virginia) and US East (Ohio). GPT-5.6 Terra and Luna are available in US East (N. Virginia), US East (Ohio), and US West (Oregon). For more details on the update, see the OpenAI blog. For the latest pricing information for GPT-5.6 models on Amazon Bedrock, please visit the Amazon Bedrock pricing page.

 

​On 7/30, OpenAI announced updated pricing for GPT-5.6 Terra and GPT-5.6 Luna. 
GPT-5.6 Terra is the balanced model for everyday production work, delivering GPT-5.5-level performance at lower cost. GPT-5.6 Luna is the fast, affordable model for high-volume inference tasks where latency and cost per token matter most. GPT-5.6 Sol pricing remains unchanged. Pricing on Amazon Bedrock matches OpenAI first-party rates, and usage counts toward your existing AWS commitments. 
GPT-5.6 Sol is available in US East (N. Virginia) and US East (Ohio). GPT-5.6 Terra and Luna are available in US East (N. Virginia), US East (Ohio), and US West (Oregon). For more details on the update, see the OpenAI blog. For the latest pricing information for GPT-5.6 models on Amazon Bedrock, please visit the Amazon Bedrock pricing page.  

AWS Transit Gateway now supports Policy-Based Routing (PBR), giving network administrators granular control over how traffic is forwarded across their AWS network. With PBR, forwarding decisions can be based on a combination of packet attributes including source and destination IP addresses, ports, and protocol rather than destination IP address alone.

Previously, customers needing traffic steering or workload isolation had to build multi-VPC architectures with additional routing hops, adding complexity and operational overhead. PBR eliminates this by extending Transit Gateway’s native routing capabilities, enabling security architects and enterprise network teams to classify and direct traffic inline without extra infrastructure. Customers associate a policy table with a Transit Gateway attachment and define an ordered set of rules. Each rule classifies traffic and directs matching packets to a specified route table using first-match-wins logic. This supports use cases such as steering sensitive workloads through AWS Network Firewall or third-party inspection appliances, routing application traffic over AWS Direct Connect or AWS VPN paths based on source, port, or protocol, and isolating production and development environments into separate routing domains to limit lateral movement.

Policy-Based Routing for AWS Transit Gateway is available in all commercial AWS Regions where Transit Gateway is available. You can configure PBR using the AWS Management Console, AWS Command Line Interface (CLI), and the AWS Software Development Kit (SDK). PBR incurs no additional charge beyond standard Transit Gateway fees. To learn more about Policy-Based Routing for AWS Transit Gateway, visit the AWS Transit Gateway product page .

 

​AWS Transit Gateway now supports Policy-Based Routing (PBR), giving network administrators granular control over how traffic is forwarded across their AWS network. With PBR, forwarding decisions can be based on a combination of packet attributes including source and destination IP addresses, ports, and protocol rather than destination IP address alone. Previously, customers needing traffic steering or workload isolation had to build multi-VPC architectures with additional routing hops, adding complexity and operational overhead. PBR eliminates this by extending Transit Gateway’s native routing capabilities, enabling security architects and enterprise network teams to classify and direct traffic inline without extra infrastructure. Customers associate a policy table with a Transit Gateway attachment and define an ordered set of rules. Each rule classifies traffic and directs matching packets to a specified route table using first-match-wins logic. This supports use cases such as steering sensitive workloads through AWS Network Firewall or third-party inspection appliances, routing application traffic over AWS Direct Connect or AWS VPN paths based on source, port, or protocol, and isolating production and development environments into separate routing domains to limit lateral movement. Policy-Based Routing for AWS Transit Gateway is available in all commercial AWS Regions where Transit Gateway is available. You can configure PBR using the AWS Management Console, AWS Command Line Interface (CLI), and the AWS Software Development Kit (SDK). PBR incurs no additional charge beyond standard Transit Gateway fees. To learn more about Policy-Based Routing for AWS Transit Gateway, visit the AWS Transit Gateway product page .  

Amazon MSK Express brokers now delivers data to Amazon S3 general purpose buckets, providing a fully managed capability to deliver Apache Kafka data in Amazon S3 for downstream processing in the easiest and most reliable way. This capability automatically scales to deliver high-throughput Kafka data to S3 with end-to-end reliability for mission-critical workloads, while reducing ingestion and delivery costs by up to 60% compared to self-managed alternatives.

Customers deliver Apache Kafka data to Amazon S3 for use cases such as log archival, compliance retention, Kafka replay, and training AI/ML models, and typically build these pipelines with self-managed connectors that grow costly and operationally complex as workloads scale, forcing teams to build or source S3 connector plugins, secure approvals to deploy them, and continually scale capacity, and apply security updates across connector fleet. With this capability, MSK Express automatically handles scaling, retries, and backpressure so customers no longer manage connector fleets or coordinate across teams. MSK Express  supports throughput of up to 10 GB/s for data delivery to Amazon S3, and manages routine operations such as capacity scaling and version upgrades without introducing delivery gaps. Additionally, customers add this delivery capability without provisioning additional broker egress throughput, which eliminates the incremental infrastructure costs that scaling connector-based pipelines typically incurs, so customers scale delivery to actual workload demand rather than provisioning for peak, achieving reliable, high-throughput delivery to Amazon S3 while removing operational overhead and lowering costs.

Amazon MSK data delivery to Amazon S3 is available today in every AWS Region where Amazon MSK Express brokers are offered. For pricing information, visit the pricing page. To learn more, visit the Amazon MSK Developer Guide and Amazon MSK AI skills.

 

​Amazon MSK Express brokers now delivers data to Amazon S3 general purpose buckets, providing a fully managed capability to deliver Apache Kafka data in Amazon S3 for downstream processing in the easiest and most reliable way. This capability automatically scales to deliver high-throughput Kafka data to S3 with end-to-end reliability for mission-critical workloads, while reducing ingestion and delivery costs by up to 60% compared to self-managed alternatives.
Customers deliver Apache Kafka data to Amazon S3 for use cases such as log archival, compliance retention, Kafka replay, and training AI/ML models, and typically build these pipelines with self-managed connectors that grow costly and operationally complex as workloads scale, forcing teams to build or source S3 connector plugins, secure approvals to deploy them, and continually scale capacity, and apply security updates across connector fleet. With this capability, MSK Express automatically handles scaling, retries, and backpressure so customers no longer manage connector fleets or coordinate across teams. MSK Express  supports throughput of up to 10 GB/s for data delivery to Amazon S3, and manages routine operations such as capacity scaling and version upgrades without introducing delivery gaps. Additionally, customers add this delivery capability without provisioning additional broker egress throughput, which eliminates the incremental infrastructure costs that scaling connector-based pipelines typically incurs, so customers scale delivery to actual workload demand rather than provisioning for peak, achieving reliable, high-throughput delivery to Amazon S3 while removing operational overhead and lowering costs.
Amazon MSK data delivery to Amazon S3 is available today in every AWS Region where Amazon MSK Express brokers are offered. For pricing information, visit the pricing page. To learn more, visit the Amazon MSK Developer Guide and Amazon MSK AI skills.  

The Gemma 4 family of open-weight models from Google DeepMind on Amazon Bedrock in AWS GovCloud (US-West). With Gemma 4, you can build generative AI applications across reasoning, multimodal understanding, agentic, and software engineering workflows.

The Gemma 4 family on Amazon Bedrock includes three variants – Gemma 4 31B, Gemma 4 26B-A4B, and Gemma 4 E2B – spanning dense and mixture-of-experts (MoE) architectures with built-in reasoning, native function calling, support for 35+ languages and multimodal input across text, image, video and audio. Gemma 4 31B is suited for reasoning- and coding-heavy workloads with a 256K-token context window, Gemma 4 26B-A4B targets cost- and latency-sensitive workloads, and Gemma 4 E2B is the smallest variant, designed for low-latency interactive use cases. Gemma 4 runs on a new innovation in Amazon Bedrock designed for price performance, with improved support for tool calling, structured output, reasoning, and response streaming, so customers can build reliable generative AI applications with open-source models.

To get started, visit Gemma 4 model detail pages in our documentation.

 

​The Gemma 4 family of open-weight models from Google DeepMind on Amazon Bedrock in AWS GovCloud (US-West). With Gemma 4, you can build generative AI applications across reasoning, multimodal understanding, agentic, and software engineering workflows.
The Gemma 4 family on Amazon Bedrock includes three variants – Gemma 4 31B, Gemma 4 26B-A4B, and Gemma 4 E2B – spanning dense and mixture-of-experts (MoE) architectures with built-in reasoning, native function calling, support for 35+ languages and multimodal input across text, image, video and audio. Gemma 4 31B is suited for reasoning- and coding-heavy workloads with a 256K-token context window, Gemma 4 26B-A4B targets cost- and latency-sensitive workloads, and Gemma 4 E2B is the smallest variant, designed for low-latency interactive use cases. Gemma 4 runs on a new innovation in Amazon Bedrock designed for price performance, with improved support for tool calling, structured output, reasoning, and response streaming, so customers can build reliable generative AI applications with open-source models.
To get started, visit Gemma 4 model detail pages in our documentation.  

You can now run OpenSearch version 3.7 on Amazon OpenSearch Service. OpenSearch 3.7 introduces improvements in vector search performance, search relevance, and Query Insights.

With this launch, 1-bit scalar quantization on the Faiss and Lucene engines compresses vectors, reducing the storage and memory required by vector workloads while maintaining search accuracy. You can now retrieve vectors faster using doc values instead of document source, with no reindexing required. Search Relevance Workbench adds new evaluation metrics, CSV judgment uploads, and expanded hybrid search optimization, helping you measure and improve search quality.

This launch also introduces new Query Insights capabilities, including automated query recommendations, a finished-queries cache for observing recently completed queries, and the option to export top query data to Amazon S3, helping you identify expensive queries and analyze trends over time.

For information on upgrading to OpenSearch 3.7, please see the documentation. OpenSearch 3.7 is now available in all AWS Regions where Amazon OpenSearch Service is available.

 

​You can now run OpenSearch version 3.7 on Amazon OpenSearch Service. OpenSearch 3.7 introduces improvements in vector search performance, search relevance, and Query Insights.
With this launch, 1-bit scalar quantization on the Faiss and Lucene engines compresses vectors, reducing the storage and memory required by vector workloads while maintaining search accuracy. You can now retrieve vectors faster using doc values instead of document source, with no reindexing required. Search Relevance Workbench adds new evaluation metrics, CSV judgment uploads, and expanded hybrid search optimization, helping you measure and improve search quality.
This launch also introduces new Query Insights capabilities, including automated query recommendations, a finished-queries cache for observing recently completed queries, and the option to export top query data to Amazon S3, helping you identify expensive queries and analyze trends over time.
For information on upgrading to OpenSearch 3.7, please see the documentation. OpenSearch 3.7 is now available in all AWS Regions where Amazon OpenSearch Service is available.  

AWS Glue now supports VPC connections, filter pushdown, and partition support for the REST API connector. The REST API connector enables you to ingest data from any source that exposes a REST-based API, including proprietary systems and emerging platforms without native AWS Glue connectors. With this launch, you can operate your ETL pipelines from data sources with REST API endpoints by securely connecting to private endpoints, transfering only the data they need, and parallelizing reads for faster ingestion, all without writing custom code

With VPC support, you can use the REST API connector to access data sources hosted in private subnets or connected through VPNs or AWS PrivateLink, without exposing traffic to the public internet. Filter pushdown translates your query predicates into API-native parameters, so only matching records leave the source, reducing data transfer costs and improving job performance. Partition support splits large datasets across multiple Spark workers using field-based or record-count strategies, providing parallel reads that reduce ingestion time for high-volume, paginated APIs.

These capabilities are available in all AWS commercial regions where AWS Glue is available.

To get started, visit the AWS Glue REST API connector documentation.

 

​AWS Glue now supports VPC connections, filter pushdown, and partition support for the REST API connector. The REST API connector enables you to ingest data from any source that exposes a REST-based API, including proprietary systems and emerging platforms without native AWS Glue connectors. With this launch, you can operate your ETL pipelines from data sources with REST API endpoints by securely connecting to private endpoints, transfering only the data they need, and parallelizing reads for faster ingestion, all without writing custom code With VPC support, you can use the REST API connector to access data sources hosted in private subnets or connected through VPNs or AWS PrivateLink, without exposing traffic to the public internet. Filter pushdown translates your query predicates into API-native parameters, so only matching records leave the source, reducing data transfer costs and improving job performance. Partition support splits large datasets across multiple Spark workers using field-based or record-count strategies, providing parallel reads that reduce ingestion time for high-volume, paginated APIs. These capabilities are available in all AWS commercial regions where AWS Glue is available. To get started, visit the AWS Glue REST API connector documentation.  

Amazon EC2 Auto Scaling now supports Instance Refresh as a new AWS CloudFormation update policy. When you configure the new AutoScalingInstanceRefresh update policy and update properties that require instance replacement, CloudFormation automatically triggers an Instance Refresh.

With this integration, you can now access Instance Refresh capabilities including replace root volume for in-place updates, launch-before-terminate, alarm-based monitoring, and checkpoints with bake time for controlled rollouts. Auto Scaling features such as scaling policies and health checks remain active throughout the update, so your service health is not at risk during deployments. Rollback is handled through CloudFormation stack rollback.

This feature is available in all AWS Regions at no additional cost. To learn more, see AutoScalingInstanceRefresh update policy in the AWS CloudFormation Template Reference.

 

​Amazon EC2 Auto Scaling now supports Instance Refresh as a new AWS CloudFormation update policy. When you configure the new AutoScalingInstanceRefresh update policy and update properties that require instance replacement, CloudFormation automatically triggers an Instance Refresh.
With this integration, you can now access Instance Refresh capabilities including replace root volume for in-place updates, launch-before-terminate, alarm-based monitoring, and checkpoints with bake time for controlled rollouts. Auto Scaling features such as scaling policies and health checks remain active throughout the update, so your service health is not at risk during deployments. Rollback is handled through CloudFormation stack rollback.
This feature is available in all AWS Regions at no additional cost. To learn more, see AutoScalingInstanceRefresh update policy in the AWS CloudFormation Template Reference.  

Today, AWS WAF adds pre-parse text transformations for query arguments and ten new text transformations for use in any rule statement. Both help you normalize request content so that AWS WAF inspects requests the same way your application interprets them.

Pre-parse text transformations normalize a raw query string before AWS WAF parses it into key-value pairs, closing HTTP parameter pollution and parser differential evasion gaps. You can chain up to ten transformations, including URL decode, Combine Duplicate Query Arguments by Comma, and Replace Semicolons with Ampersands, then layer standard post-parse transformations on top within a single rule statement.

The new text transformations give you more ways to normalize content before inspection, including industry-standard options such as Uppercase, Trim, Remove Whitespace, and SHA256, plus operating-system-aware command line and JavaScript decoding functions developed by the Amazon Threat Research Team.

Each new transformation consumes 10 WCUs, with no additional charge beyond standard AWS WAF pricing, and is available in all AWS Regions. To get started, see the following resources:

• Pre-parse text transformations in AWS WAF: https://docs.aws.amazon.com/waf/latest/developerguide/waf-rule-statement-preparse-transformation.html

• Text transformations in AWS WAF: https://docs.aws.amazon.com/waf/latest/developerguide/waf-rule-statement-transformation.html

• Getting started with AWS WAF: https://docs.aws.amazon.com/waf/latest/developerguide/getting-started.html

 

​Today, AWS WAF adds pre-parse text transformations for query arguments and ten new text transformations for use in any rule statement. Both help you normalize request content so that AWS WAF inspects requests the same way your application interprets them.
Pre-parse text transformations normalize a raw query string before AWS WAF parses it into key-value pairs, closing HTTP parameter pollution and parser differential evasion gaps. You can chain up to ten transformations, including URL decode, Combine Duplicate Query Arguments by Comma, and Replace Semicolons with Ampersands, then layer standard post-parse transformations on top within a single rule statement.
The new text transformations give you more ways to normalize content before inspection, including industry-standard options such as Uppercase, Trim, Remove Whitespace, and SHA256, plus operating-system-aware command line and JavaScript decoding functions developed by the Amazon Threat Research Team.
Each new transformation consumes 10 WCUs, with no additional charge beyond standard AWS WAF pricing, and is available in all AWS Regions. To get started, see the following resources:

Pre-parse text transformations in AWS WAF: https://docs.aws.amazon.com/waf/latest/developerguide/waf-rule-statement-preparse-transformation.html

Text transformations in AWS WAF: https://docs.aws.amazon.com/waf/latest/developerguide/waf-rule-statement-transformation.html

Getting started with AWS WAF: https://docs.aws.amazon.com/waf/latest/developerguide/getting-started.html  

Amazon Redshift Data API introduces new capabilities that reduce the number of API calls to retrieve SQL statement metadata or results, provide visibility into sessions, and allow batch statements to execute on separate transactions.

Long polling: Long polling enables you to retrieve SQL statement metadata or results without polling repeatedly until the SQL statement reaches a terminal state, by delaying returning a synchronous response until the SQL statement finishes. To use this feature, specify the WaitTimeSeconds parameter on ExecuteStatement, BatchExecuteStatement, DescribeStatement, GetStatementResult, or GetStatementResultV2.

ListSessions: Applications that reuse sessions across multiple queries can now enumerate active sessions and filter by status, compute target, or database, eliminating the need to track session identifiers externally.

Flexible batch execution: BatchExecuteStatement now supports an ExecutionMode parameter with AUTO_COMMIT mode, allowing each SQL statement in a batch to execute independently so a single failure no longer rolls back the entire batch — useful for ETL pipelines and administrative scripts where partial completion is acceptable. In addition, BatchExecuteStatement now accepts an array of SqlParameter, enabling parameter reuse across all statements in a batch: define parameters once and reference them in any statement, eliminating the need to embed literal values in each query.

These features are generally available for Amazon Redshift Provisioned and Amazon Redshift Serverless in all AWS commercial and AWS GovCloud (US) Regions that support Amazon Redshift Data API. To get started, visit the Amazon Redshift Data API developer guide.

 

​Amazon Redshift Data API introduces new capabilities that reduce the number of API calls to retrieve SQL statement metadata or results, provide visibility into sessions, and allow batch statements to execute on separate transactions. Long polling: Long polling enables you to retrieve SQL statement metadata or results without polling repeatedly until the SQL statement reaches a terminal state, by delaying returning a synchronous response until the SQL statement finishes. To use this feature, specify the WaitTimeSeconds parameter on ExecuteStatement, BatchExecuteStatement, DescribeStatement, GetStatementResult, or GetStatementResultV2. ListSessions: Applications that reuse sessions across multiple queries can now enumerate active sessions and filter by status, compute target, or database, eliminating the need to track session identifiers externally. Flexible batch execution: BatchExecuteStatement now supports an ExecutionMode parameter with AUTO_COMMIT mode, allowing each SQL statement in a batch to execute independently so a single failure no longer rolls back the entire batch — useful for ETL pipelines and administrative scripts where partial completion is acceptable. In addition, BatchExecuteStatement now accepts an array of SqlParameter, enabling parameter reuse across all statements in a batch: define parameters once and reference them in any statement, eliminating the need to embed literal values in each query. These features are generally available for Amazon Redshift Provisioned and Amazon Redshift Serverless in all AWS commercial and AWS GovCloud (US) Regions that support Amazon Redshift Data API. To get started, visit the Amazon Redshift Data API developer guide.  

AWS announces the general availability (GA) of AWS Interconnect — multicloud with Oracle Cloud Infrastructure (OCI).

Customers have been adopting multicloud strategies while migrating more applications to the cloud. They do so for many reasons including interoperability requirements, the freedom to choose technology that best suits their needs, and the ability to build and deploy applications on any environment with greater ease and speed. Previously, when interconnecting workloads across multiple cloud providers (CSPs), customers had to go the route of a ‘do-it-yourself’ multicloud approach, leading to complexities of building and managing global multi-layered networks at scale. AWS Interconnect – multicloud is the first purpose-built product of its kind and a new way of how clouds connect and talk to each other, allowing customers to quickly provision resilient, scalable private connections to other cloud providers.

In May, OCI launched support for AWS Interconnect in public preview and became the latest CSP to adopt the open specification that powers the service. With today’s GA launch, AWS customers can now rely on the same consistent, simple experience to interconnect their workloads on OCI and Google Cloud. Microsoft Azure will launch later in 2026.

Interconnect – multicloud is available with OCI in the us-east-1 (N. Virginia) AWS Region. You can create an Interconnect using the AWS Management Console, Command Line Interface (CLI), or API. For more information, see the AWS Interconnect – multicloud documentation.

 

​AWS announces the general availability (GA) of AWS Interconnect — multicloud with Oracle Cloud Infrastructure (OCI).
Customers have been adopting multicloud strategies while migrating more applications to the cloud. They do so for many reasons including interoperability requirements, the freedom to choose technology that best suits their needs, and the ability to build and deploy applications on any environment with greater ease and speed. Previously, when interconnecting workloads across multiple cloud providers (CSPs), customers had to go the route of a ‘do-it-yourself’ multicloud approach, leading to complexities of building and managing global multi-layered networks at scale. AWS Interconnect – multicloud is the first purpose-built product of its kind and a new way of how clouds connect and talk to each other, allowing customers to quickly provision resilient, scalable private connections to other cloud providers.
In May, OCI launched support for AWS Interconnect in public preview and became the latest CSP to adopt the open specification that powers the service. With today’s GA launch, AWS customers can now rely on the same consistent, simple experience to interconnect their workloads on OCI and Google Cloud. Microsoft Azure will launch later in 2026. Interconnect – multicloud is available with OCI in the us-east-1 (N. Virginia) AWS Region. You can create an Interconnect using the AWS Management Console, Command Line Interface (CLI), or API. For more information, see the AWS Interconnect – multicloud documentation.