I introduced readers to Amazon Bedrock a few months ago. Bedrock is a (relatively) new machine learning (ML) service that provides a managed foundation for building and deploying large language models (LLMs). One of the key features of Bedrock is its ability to enable cross-region inference, allowing customers to leverage pre-trained LLMs hosted in one AWS region to serve inference requests from other AWS regions. The blog is at https://aws.amazon.com/blogs/machine-learning/getting-started-with-cross-region-inference-in-amazon-bedrock/
Cross Region Inferencing for Machine Learning Applications
Cross-region inference in Bedrock can be beneficial for customers who need to deploy their ML-powered applications closer to their end-users, reducing latency and improving the overall user experience. It also offers increased availability and fault tolerance by allowing customers to maintain model availability even if one region experiences an outage.
In this blog post, the authors provide a step-by-step guide on how to get started with cross-region inference in Amazon Bedrock. They cover the key concepts, explain the setup process, and demonstrate how to deploy and use a pre-trained model across multiple AWS regions. The post also touches on the cost implications and best practices for effective cross-region inference.
I will do another post covering this capability step by step but to set up cross-region inference, customers first need to create a Bedrock model in their desired source region. They can then use the Bedrock API to configure the model for cross-region inference, specifying the target regions where they want to serve inference. Bedrock handles the necessary data replication and network configuration to enable low-latency, high-availability inference across regions. Customers can then integrate their applications with the cross-region Bedrock model using standard API calls, without needing to worry about the underlying regional infrastructure. The blog post provides detailed steps and code examples to help customers get started with this functionality.
Key Benefits
- Elastic Scaling Across Regions: Cross-region inference in Amazon Bedrock allows customers to leverage compute capacity from multiple AWS regions to handle bursts and spikes in generative AI workloads. This enables seamless scaling to meet fluctuating demand without the need for manual capacity planning or provisioning.
- Consistent API Integration: The cross-region inference functionality is fully compatible with the existing Amazon Bedrock API, allowing customers to integrate with the service using their existing application code and workflows.
- Cost Optimization: There are no additional routing or data transfer costs associated with cross-region inference. Customers are charged the same per-token price for models, regardless of whether the inference requests are served from the source or target regions.
- Improved Resilience: By distributing workloads across multiple AWS regions, cross-region inference enhances the overall resilience of generative AI applications. This helps customers focus on their core application logic rather than managing traffic bursts or potential regional outages.
- Flexible Region Selection: Bedrock provides a range of pre-configured AWS region sets that customers can choose from, allowing them to select the optimal regional configuration based on their specific requirements, such as latency, data residency, or availability needs.
Conclusion
This cross-region inference capability in Amazon Bedrock enables customers to build highly scalable, resilient, and cost-effective generative AI applications that can adapt to dynamic usage patterns and regional variations, all while leveraging the consistency and simplicity of the Bedrock API. This feature is a valuable add-on for AWS customers or in general anyone interested in leveraging the power of large language models in their applications and wish to deploy across the globe.