Technology Overview

By 2025, private and embedded LLMs has moved from exotic to practical. The combination of powerful open-source models, mature deployment tooling, and commodity hardware capable of running them means that retailers face a genuine choice: public APIs, private deployment, or hybrid architectures combining both.

The choice comes down to constraints. If you have high volume, sensitive data, compliance requirements, or offline operation needs, private deployment made sense. If you have variable traffic, need cutting-edge capabilities, or want to avoid operational complexity, public APIs remain attractive. Most large retailers end up with hybrid architectures, using each approach where it fit best.

What has changed isn’t just the technology. It is the recognition that AI infrastructure deserves the same architectural consideration as any other critical system. You wouldn’t send all your data through a third-party API for processing. You wouldn’t build every feature as a web service call to an external provider. AI shouldn’t be different. Sometimes the right answer is bringing the capability into your stack, accepting the operational complexity in exchange for control, performance, and flexibility.

The retailers who figured this out early, who invested in the infrastructure and expertise to run AI on their own terms, have a genuine competitive advantage. Not because their models were better, though sometimes they were. But because they have the flexibility to deploy AI wherever their business needs it, from the cloud to the store floor to the handheld device, without architectural constraints limiting what is possible.