Deploy a mixed-placement AI shopping assistant on Google Kubernetes Engine with Axion-based compute
Introduction
Understand mixed placement for a storefront AI assistant
Set up the source tree and cluster access
Deploy and validate the storefront baseline on the Google N4A node pool
Review the shopping assistant implementation
Build and push the assistant image to Artifact Registry
Deploy the assistant on the Google N4A node pool
Observe and benchmark the assistant on the Google N4A node pool
Move the assistant to the Google C4A node pool and compare results
Next Steps

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Build one Arm image for both Axion placements

You need only one linux/arm64 assistant image for both the N4A and C4A pools, and that image can run in either placement.

Note

This is an Arm-targeted build for Axion, not a full multi-architecture build. A multi-architecture image is useful when the same tag must support non-Arm environments such as linux/amd64.

If you opened a new terminal, return to the source tree and restore the required variables:

    

        
        
source "${HOME}/.storefront-axion-env"
cd "${REPO}"

Confirm the image name

Confirm the reusable image path you configured during setup:

    

        
        
echo "${ASSISTANT_IMAGE}"

Use buildx to build and push the assistant image

Build the assistant image for linux/arm64 and push it to Artifact Registry:

    

        
        
docker buildx build --platform linux/arm64 -t "${ASSISTANT_IMAGE}" --push src/shoppingassistantservice

The command packages the Flask assistant service, protobuf stubs, and Python dependencies. The important success signal is that the build finishes and pushes the image to your Artifact Registry path.

Verify the pushed tag

List the tags for the assistant image:

    

        
        
gcloud artifacts docker tags list "${ASSISTANT_IMAGE_REPO}"

The output includes the lab-v1 tag.

What you’ve accomplished and what’s next

You’ve now built and published an Arm image for the shopping assistant. Kubernetes can now pull this image when you deploy the assistant on N4A.

Next, you’ll create the N4A overlay and add the assistant to the running storefront.

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