Real-world success with Spanner’s fully interoperable multi-model database

In the first post on the power of multi-model databases to lay the foundations for gen AI, we highlighted how Google Cloud Spanner helps organizations overcome some  of the challenges presented by traditional approaches to database architecture and management. In this post, we dive deeper on the specific examples, across four common use cases. We are seeing customers increasingly choose Spanner's multi-model capabilities to address three key strategic goals: A foundation of scale and reliability: Many specialized databases for graph, vector, or search, are built on traditional, single-machine architectures. As a result, they face fundamental challenges with scalability, availability, and consistency. We see customers migrate off these specialized systems because they have - or are about to - hit a wall. All Spanner’s data models are built on its tried-and-true platform offering 99.999% availability, automatic scaling, and limitless horizontal scale, and they can easily extend to new ca