Enhancing AI Performance with Global Cross-Region Inference in Amazon Bedrock
Introduction
Organizations are increasingly integrating generative AI capabilities into their applications to enhance customer experiences, streamline operations, and drive innovation. As generative AI workloads continue to grow, challenges regarding performance, reliability, and availability arise, particularly for scaling AI inference workloads across AWS Regions.
Global Cross-Region Inference Launch
To address the need for consistent performance, we introduced cross-Region inference (CRIS) for Amazon Bedrock, a managed capability designed to automatically route inference requests across multiple AWS Regions. This enables seamless handling of traffic bursts and enhanced throughput without complex load-balancing interventions.
Why Global Cross-Region Inference Matters
We are excited to announce the availability of global cross-Region inference with Anthropic’s Claude Sonnet 4.5 on Amazon Bedrock. This feature allows organizations to choose geography-specific or global inference profiles, automatically selecting optimal Regions for processing requests and optimizing resource use.
Core Functionality
This section delves into the mechanisms behind global cross-Region inference, featuring automatic request routing, monitoring and logging capabilities, and the importance of data security and compliance.
Implementing Global Cross-Region Inference
Learn how to utilize global CRIS with Anthropic’s Claude Sonnet 4.5, including code examples, IAM policy requirements, and configuration steps for seamless integration.
Request Limit Increases
Understanding service quotas and how to request limit increases for global CRIS is vital for organizations to ensure they can scale effectively.
Conclusion
Amazon Bedrock’s global cross-Region inference offers a significant evolution in generative AI capabilities, delivering resilience and flexibility for high-volume workloads. Organizations are encouraged to try this powerful feature to optimize their AI applications.
About the Authors
Meet the experts behind this initiative, who specialize in generative AI, cloud-based architectures, and machine learning solutions, dedicated to elevating AI capabilities across industries.
Streamlining AI Workloads with Global Cross-Region Inference on Amazon Bedrock
Organizations are increasingly turning to generative AI capabilities to enhance customer experiences, streamline operations, and drive innovation. As these AI workloads grow in scale and importance, maintaining performance, reliability, and availability becomes paramount. Customers are now looking for ways to scale their AI inference workloads across multiple AWS Regions to ensure consistent performance and reliability.
To meet this demand, we’ve introduced Cross-Region Inference (CRIS) for Amazon Bedrock. This managed capability ensures that inference requests are automatically routed across multiple Regions, allowing applications to seamlessly handle traffic bursts and achieve higher throughput without requiring developers to predict demand fluctuations or implement complex load-balancing systems.
In this post, we explore how global cross-Region inference works, the benefits it provides, and how organizations can implement it using Anthropic’s Claude Sonnet 4.5 to elevate their AI applications’ performance and reliability.
Core Functionality of Global Cross-Region Inference
Understanding Inference Profiles
An inference profile in Amazon Bedrock defines a foundation model (FM) and the Regions toward which model invocation requests can be routed. The global cross-Region inference profile for Claude Sonnet 4.5 takes this a step further, allowing requests to be routed to supported commercial Regions worldwide to prepare for unplanned traffic bursts effectively.
Intelligent Request Routing
Global CRIS employs an intelligent request routing mechanism that considers various factors to ensure optimal processing of inference requests, including:
- Regional Capacity: The system evaluates the current load and available capacity in potential destination Regions.
- Latency Considerations: While availability is prioritized, the system also takes latency into account, smoothly routing requests when necessary.
- Availability Metrics: Continuous monitoring of FM availability across Regions supports optimal routing decisions.
This intelligent routing allows Amazon Bedrock to distribute traffic dynamically, providing optimal availability for every request and improving performance during peak usage periods.
Monitoring and Logging
Using global CRIS, Amazon CloudWatch and AWS CloudTrail continue to log entries only in the source Region where the request originates. This simplifies monitoring by maintaining all records in a single Region, despite where the request is processed. Enhanced insights into how requests are distributed across AWS’s global infrastructure are available through CloudTrail.
Data Security and Compliance
Global CRIS upholds high data security standards. All data transmitted during the inference process remains encrypted within the secure AWS network. Organizations can opt for geography-specific inference profiles to meet specific data residency or compliance requirements, providing flexibility that balances redundancy with compliance needs.
Implementing Global Cross-Region Inference
To leverage global CRIS with Anthropic’s Claude Sonnet 4.5, developers need to complete the following steps:
- Use the Global Inference Profile ID: Specify the global Anthropic inference profile ID in your API calls.
- Configure IAM Permissions: Ensure appropriate AWS Identity and Access Management (IAM) permissions for accessing the inference profile and FMs in potential destination Regions.
Here’s an example code snippet to update your application:
import boto3
bedrock = boto3.client('bedrock-runtime', region_name="us-east-1")
model_id = "global.anthropic.claude-sonnet-4-5-20250929-v1:0"
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'])Advantages of Global Cross-Region Inference
By utilizing global CRIS with Claude Sonnet 4.5, organizations can realize substantial benefits over traditional geographic cross-Region inference profiles:
- Enhanced Throughput: Global CRIS handles traffic spikes automatically, ensuring optimal performance for critical applications during peak demand.
- Cost Efficiency: Organizations benefit from a pricing model that offers savings on input and output tokens compared to geographic inference, maximizing resource utilization.
- Streamlined Monitoring: Maintain a centralized view of application performance and usage patterns through familiar AWS monitoring tools.
- On-demand Quota Flexibility: With global CRIS, workloads can draw from a larger pool of resources, facilitating efficient handling of high-volume requests.
Conclusion
Amazon Bedrock’s global cross-Region inference for Anthropic’s Claude Sonnet 4.5 represents a significant enhancement in AWS generative AI capabilities. The ability to route inference requests globally empowers organizations to optimize their AI applications, improve performance during high-traffic periods, and ensure robust compliance and security standards.
By implementing this capability, businesses can experience firsthand the benefits of streamlined operations and improved reliability. For a deeper dive into global cross-Region inference, visit the provided links for further information.
About the Authors
This blog post is brought to you by a team of experts at AWS, including solutions architects, product managers, and software engineers, who specialize in generative AI, cloud architecture, and machine learning applications.
Get started with global cross-Region inference on Amazon Bedrock today and elevate your AI solutions to new heights!