Embracing gen AI with the latest Google Cloud databases innovations
Embracing gen AI means providing delightful AI-driven experiences to your users. As you take this journey, databases provide the foundation for building enterprise gen AI apps that are accurate, relevant and grounded in enterprise truth. At Google Cloud, we’re focused on helping you in three key ways.
First, helping developers build intelligent apps with operational data. Second, leveraging gen AI — and specifically our Gemini models — to simplify every stage of the database journey, including migrations, fleet management, troubleshooting, and performance optimization. And third, modernizing your databases so you can take advantage of gen AI. Let’s see how all three come to life.
Developers play a key role in the success of gen AI in the enterprise. According to the Bureau of Labor Statistics , there are 10x more software developers compared to data scientists — an order of magnitude more technical practitioners who can innovate with AI. That’s why we’re excited about the innovations we've brought to developers over the past year: vector search, LangChain integration , and foundation models are supported across all our databases. We’ve also added powerful new features all in a single database, Spanner - the always-on, globally consistent, and virtually unlimited scale database. These features include graph processing , advanced full-text search , and vector search — so you can build a new class of smart, contextually relevant applications that rely on interconnected data and semantic search.
We’ve also enhanced the gen AI capabilities in AlloyDB , the database that combines the best of Google with 100% PostgreSQL compatibility. Today, we’re announcing that the ScaNN vector index in AlloyDB is now generally available. This is the same technology that powers Google’s most popular services including Google Search and YouTube, infusing 12 years of research and innovation into AlloyDB. The ScaNN index is the first PostgreSQL-compatible index that can scale to support more than one billion vectors while maintaining state-of-the-art query performance — enabling high performance workloads for every enterprise.
