The rise of AI is transforming how cloud applications are built. However, many engineers still struggle with the practical question: how do you integrate AI capabilities, such as semantic understanding and contextual reasoning, into cloud-native systems? Traditional databases and keyword searches aren’t enough when dealing with unstructured data such as documents, logs, or user interactions. The key lies in embeddings and the vector databases designed to work with them.

In this session, we will break down the concepts of embeddings and vector databases, showing how they can be generated with Vertex AI and stored, indexed, and queried using tools like AlloyDB with pgvector or managed vector databases on GCP. We’ll explore real-world data cleaning strategies and highlight how to integrate vector search into production-ready pipelines.

To make these concepts tangible, we’ll demo a Retrieval-Augmented Generation (RAG) application that combines LLMs with vector databases on GCP, powering semantic search over unstructured content. This end-to-end example will show attendees how embeddings and vector databases can unlock intelligent features in everyday applications.

This talk is designed for cloud engineers, DevOps practitioners, and software developers who want to move beyond the hype and build AI-native applications on GCP. Attendees will walk away with both the architectural patterns and a live demo showing how to deploy intelligent, production-ready systems with embeddings and vector search.