Vertex AI region vs Google Search region — are they the same?

Understanding Region Selection in Google Cloud’s Vertex AI and Google Search

When integrating AI solutions into your applications, especially those leveraging Google’s Vertex AI and Google Search APIs, it’s essential to understand how regional configurations influence the behavior and results your users receive.

Region Specification in Vertex AI

Vertex AI, Google Cloud’s comprehensive machine learning platform, allows developers to specify the geographical location of their resources. For example, when configuring the API, you can set the location parameter (such as europe-west9) to determine where your models and compute resources are hosted. This regional setting can impact latency, compliance, and data sovereignty.

Google Search Grounding and Regional Behavior

In the context of incorporating Google Search as a grounding mechanism for language models—like Gemini’s API—there’s a pertinent consideration: does the region specified in your API requests influence the search results? Specifically, if you set the location parameter to europe-west9, which is located in Europe, does this configuration ensure that search results are tailored to the user’s actual geographic location, such as France?

Key Clarification

Currently, the regional setting in Vertex AI primarily affects the backend infrastructure and computational resources. It determines where your models run and how data is managed within Google’s cloud environment. However, the impact of this setting on the search results itself is less direct.

The Google Search API utilizes its own regional and language targeting mechanisms. Typically, the search experience can be influenced by parameters such as gl (geolocation) and hl (language), which you can explicitly specify in your API requests to tailor search results based on user location or language preferences.

Implications for Developers

• Region Setting in Vertex AI: Useful for optimizing model deployment and ensuring compliance with regional data regulations.
• Search Result Localization: Achieved through API parameters like gl and hl, allowing results to be more relevant to the user’s actual location, regardless of the compute resource region.

Best Practice Recommendations

• To ensure search results are pertinent to a specific geographic area, explicitly specify the gl parameter with the country code that matches your user’s location (e.g., fr for France).
• Continue to set your location parameter in Vertex AI for infrastructure and compliance reasons, but do not rely solely on it for search result localization.
• Test your implementation to verify that search grounding aligns with your localization goals.

Conclusion

While configuring the location parameter in Vertex AI determines where your models and