The competitive advantage of having access to large language models (LLMs) in a timely manner cannot be understated in the rapidly evolving field of artificial intelligence (AI). However, many organizations face challenges when it comes to the regional availability of these models, which can hinder their innovation process. Snowflake has addressed this critical obstacle by announcing the general availability of cross-region inference, allowing developers to process requests on Cortex AI even when models are not yet available in their source region. This breakthrough enables organizations to integrate new LLMs as soon as they become accessible, bridging the gap created by regional restrictions.
To facilitate cross-region inference on Cortex AI, developers need to enable the feature and specify the regions for inference processing. This setting is crucial for data traversal between regions, especially when operating on different cloud providers. When both regions are on Amazon Web Services (AWS), data is securely transmitted across the global network with automatic encryption at the physical layer. In contrast, if regions are on different cloud platforms, traffic is encrypted and traverses the public internet through mutual transport layer security (MTLS). Notably, inputs, outputs, and service-generated prompts are not stored or cached during the inference process, ensuring data privacy and security.
Configuring account-level parameters is essential for determining where inference processing occurs within the secure Snowflake perimeter. Cortex AI automates the selection of a processing region if the requested LLM is unavailable in the source region, streamlining the deployment process for developers. For example, specifying parameters such as “AWS_US” directs the inference to U.S. east or west regions, while choosing “AWS_EU” routes the processing to central EU or Asia Pacific northeast. However, it’s important to note that target regions are currently limited to AWS, meaning that cross-region requests enabled in Azure or Google Cloud will still process in AWS.
A key advantage of cross-region inference on Cortex AI is its simplicity and efficiency in executing tasks with minimal code. By leveraging a single line of code, users can seamlessly integrate with the LLM of their choice, regardless of regional availability. This streamlined approach not only accelerates the development process but also reduces the complexity associated with deploying AI models across different regions. Furthermore, users are charged credits based on the usage of the LLM in the source region, ensuring cost-effectiveness and transparency in billing practices.
The introduction of cross-region inference by Snowflake marks a significant milestone in overcoming regional limitations and fostering a more collaborative and efficient AI development environment. By enabling seamless integration of LLMs across different regions, organizations can accelerate their innovation journey and stay ahead of the competition in today’s fast-paced AI landscape. As advancements in AI continue to push boundaries and drive transformative changes, cross-region inference emerges as a critical enabler for unlocking the full potential of large language models in a global context.
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