Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)

We’re excited to announce the availability of Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 through Amazon Bedrock global cross-Region inference for customers operating in the Middle East. This launch supports organizations in the Middle East to access Anthropic’s latest Claude models on Amazon Bedrock while benefiting from global, highly available inference routing across the AWS network. With global cross-Region inference, you can scale inference workloads seamlessly, improve resiliency, and reduce operational complexity.

To help you achieve the scale of your AI applications, Amazon Bedrock offers cross-Region inference profiles, a powerful feature organizations can use to seamlessly distribute inference processing across multiple AWS Regions. This capability helps you get higher throughput while you’re building at scale and helps keep your generative AI applications responsive and reliable even under heavy load. When you invoke a cross-Region inference profile in Amazon Bedrock, your request follows an intelligent routing path. The request originates from your source Region where you make the API call and is automatically routed to one of the destination Regions defined in the inference profile. Cross-Region inference operates through the secure AWS network with end-to-end encryption for data in transit.

The key distinction is that cross-Region inference doesn’t change where data is stored—customer data is not stored in a destination Region when using cross-Region inference; customer-managed logs (such as model invocation logging), knowledge bases, and stored configurations remain exclusively within the source Region. The inference request travels over the AWS Global Network managed by Amazon Bedrock, and responses are returned encrypted to your application in the source Region.

In this post, we discuss how to use global cross-Region inference in Amazon Bedrock for Anthropic Claude models in the Middle East. We guide you through the capabilities of each Anthropic Claude model variant, the key advantages of global cross-Region inference including improved resilience, real-world use cases you can implement, and a code example to help you start building generative AI applications immediately.

Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 on Amazon Bedrock

The latest generation of Anthropic’s Claude models are now available on Amazon Bedrock in the Middle East (UAE) and Middle East (Bahrain) Regions. The new Claude Opus 4.6 brings advanced capabilities to Amazon Bedrock customers, including industry-leading performance for agentic tasks, complex coding projects, and enterprise-grade workflows that require deep reasoning and reliability. Claude Sonnet 4.6 balances intelligence with speed and cost-efficiency for production-ready applications and multi-step tasks. Claude Haiku 4.5 focuses on low-latency responses for real-time use cases like AI assistants and high-volume content generation. By combining these models with global cross-Region inference, you can dynamically scale your AI workloads across Regions while maintaining optimal performance. This helps organizations select the right model for their specific requirements—whether prioritizing intelligence, speed, or cost—while benefiting from seamless scaling and improved availability across global infrastructure.

The following table summarizes the available models and their source and destination Regions.

Benefits of global cross-Region inference

As generative AI adoption accelerates, customers increasingly require the ability to scale inference workloads reliably while maintaining consistent performance. Deploying large-scale generative AI applications often involves managing Regional capacity constraints, traffic spikes, and availability requirements. Amazon Bedrock global cross-Region inference addresses these challenges by allowing inference requests to be automatically routed to the optimal Region within a predefined global inference profile, helping deliver multiple advantages:

• Enhanced throughput during peak demand – For organizations in the Middle East, global cross-Region inference provides critical resilience during Regional peak periods, such as Ramadan, major shopping events, or high-traffic business hours. The system automatically routes requests to Regions with available capacity across the global infrastructure, making sure your applications maintain performance even during unexpected traffic surges. This dynamic routing happens seamlessly, and traffic routing is fully managed by Amazon Bedrock. For business-critical applications serving customers across the GCC and broader MENAT Region, this means avoiding costly downtime or degraded performance that could impact revenue and customer trust.
• Secure data transmission – The data transmitted during cross-Region operations is managed by Amazon Bedrock. Data is encrypted in transit between Regions, helping meet the stringent security and data protection requirements important to organizations in the Middle East.
• Simplified multi-Region strategy – Organizations no longer need to architect complex multi-Region deployments manually. Global cross-Region inference helps provide enterprise-grade resilience without the operational overhead of managing multiple Regional endpoints.
• Support for rapid digital transformation – As Middle East organizations accelerate their digital transformation initiatives aligned with national visions (like Saudi Vision 2030 and UAE’s AI Strategy), global cross-Region inference provides the scalability needed to support ambitious AI projects without capacity constraints.
• Streamlined monitoring – Amazon CloudWatch and AWS CloudTrail continue to record the log entries in your Middle East source Region, providing a centralized view of your application’s performance. This simplified observability means your teams can monitor and manage generative AI applications using familiar AWS tools, regardless of where requests are processed globally, making compliance and operational management more straightforward.
• On-demand quota flexibility – Global cross-Region inference helps remove the constraints of individual Regional capacity limits. Your workloads can dynamically access resources across the AWS global infrastructure, making it seamless to handle high-volume applications and sudden traffic spikes common in the rapidly growing digital economy of the Region.

With this capability now available for Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 in the Middle East, organizations across the Region can build and scale generative AI applications with greater confidence, knowing they can access enterprise-grade resilience and performance.

Global inference use cases

The availability of Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 through global cross-Region inference unlocks a wide range of use cases for customers in the Middle East, including:

• Enterprise copilots and AI assistants that require high availability and consistent performance
• Agentic workflows that orchestrate complex reasoning and tool usage
• Developer productivity tools for code generation, review, and transformation
• Customer engagement applications requiring elastic scale
• Advanced data analysis and document processing

Quota management

To see the default quotas for cross-Region throughput when using global inference profiles, refer to the global cross-Region model inference requests per minute and global cross-Region model inference tokens per minute values in Amazon Bedrock service quotas.

You can request, view, and manage quotas for the global cross-Region inference profile from the Service Quotas console or by using AWS Command Line Interface (AWS CLI) commands in your source Region.

Getting started

To start using Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, or Claude Haiku 4.5 with global cross-Region inference (for example, the me-central-1 Region), complete the following steps:

1. Verify your AWS Identity and Access Management (IAM) role or user has the necessary permissions to invoke Amazon Bedrock models using a cross-Region inference profile.
2. Invoke the model using the Amazon Bedrock APIs or AWS SDKs:

import boto3
import json
bedrock = boto3.client('bedrock-runtime', region_name='me-central-1')
model_id = "global.anthropic.claude-sonnet-4-6"
response = bedrock.converse(
    messages=[{"role": "user", "content": [{"text": "Explain cloud computing in 2 sentences."}]}],
    modelId=model_id,
)

print("Response:", response['output']['message']['content'][0]['text'])
print("Token usage:", response['usage'])
print("Total tokens:", response['usage']['totalTokens'])

You can monitor usage, performance, and costs through CloudWatch and AWS Cost Explorer to scale your applications as demand grows.

Conclusion

With the launch of Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 using Amazon Bedrock global cross-Region inference, customers in the Middle East can now build highly scalable, resilient generative AI applications without the operational overhead of managing Regional inference capacity. We are excited about this launch and look forward to seeing how you use these capabilities to accelerate innovation and deliver impactful AI-powered experiences across the Region. To learn more, see Getting started with cross-region inference in Amazon Bedrock.

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