Why today’s enterprises need more than just chatbots
As generative AI technology progresses, enterprises find that simple chatbots aren’t enough to meet their growing demands. Businesses now require AI applications that are intelligent and context-aware, responding to real-time data and operational conditions.
Enterprises seek to leverage AI tools that can handle increasingly complex tasks like automated customer support, intelligent decision-making systems, and advanced data analytics. The evolution of AI technology means that companies must move beyond basic automation and towards solutions that can interpret, learn, and act on data in a way that adds genuine value to business processes.
The rise of cloud hyperscalers—large-scale cloud service providers—supports this shift. These companies are competing to provide enterprises with the infrastructure needed to build next-generation AI solutions.
Through focusing on improving database technologies and AI tools, providers like Google Cloud are equipping businesses with the necessary resources to deploy sophisticated AI applications faster and more efficiently.
AI is evolving fast and enterprises are demanding more power
Enterprises are seeing a rapid evolution in AI capabilities, driving them to seek more powerful and capable solutions. Simple chatbots, which once sufficed for basic automation and customer service tasks, are no longer enough.
Today, enterprises want AI applications that can process large volumes of operational data, adapt to changing contexts, and make informed decisions in real-time. This demand is prompting providers to innovate and deliver advanced tools that facilitate faster, smarter, and more reliable AI solutions.
Cloud giants are racing to transform enterprise AI applications
Cloud hyperscalers like Google Cloud, Amazon Web Services, and Microsoft Azure are in a race to deliver cutting-edge tools that support the deployment of AI applications at an enterprise scale.
Google Cloud, for instance, has been rolling out extensive updates to its database and AI tool offerings, making it easier for businesses to deploy and maintain advanced AI solutions. These providers offer the infrastructure required to handle large-scale AI workloads, including vector databases and memory-efficient systems designed for high-performance computing.
Their services help enterprises seamlessly integrate AI capabilities across their operations, enabling everything from improved customer service to data-driven business intelligence. With cloud-based tools, companies can build AI systems that are both powerful and scalable, adapting as their needs grow and change.
Google Cloud’s latest database updates are setting new benchmarks in AI
Google Cloud has released a series of updates that improve the performance and scalability of its AI-ready databases, particularly AlloyDB. As a fully managed PostgreSQL-compatible database, AlloyDB is designed for the modern enterprise, offering advanced features like ScaNN (scalable nearest neighbor) vector index.
The update optimizes database performance for AI tasks, for faster query responses and more efficient memory resource usage.
The ScaNN index, a technology powering Google Search and YouTube, improves index creation times and boosts the speed of vector queries, all while reducing the memory footprint—all of which are key for businesses looking to deploy AI applications that require real-time processing and quick access to large data sets.
AlloyDB is evolving with major upgrades that boost speed and memory efficiency
One of the key updates to AlloyDB is the integration of ScaNN technology, which has transformed the database’s ability to handle vector queries.
Vector databases are key for modern AI applications, such as recommender systems and chatbots, that need to search through large datasets quickly and accurately.
ScaNN accelerates these processes, offering 4x faster vector queries and 8x faster index creation, while also reducing the memory footprint by 3-4x compared to older algorithms like HNSW.
Through reducing the hardware resources needed to process AI workloads, businesses can now run more complex AI models without compromising on speed or performance. ScaNN also supports over 1 billion vectors, making it one of the most scalable options for enterprises looking to build AI-driven applications.
Google Cloud partners with Aiven to bring AlloyDB Omni to every platform
Google Cloud has expanded its partnership with Aiven to offer AlloyDB Omni as a managed service, providing businesses with more flexible deployment options. AlloyDB Omni can now be deployed across different cloud platforms or even on-premises environments, giving enterprises the ability to manage transactional, analytical, and vector workloads on a single platform.
Flexibility here allows companies to build generative AI applications tailored to their specific operational environments. Whether running on Google Cloud, other cloud services, or in private data centers, enterprises can benefit from AlloyDB’s advanced features without being locked into a single infrastructure provider.
ScaNN brings Google Search and YouTube tech to AlloyDB
Vector databases are becoming more important for AI workloads, including systems that rely on recommendation engines or chatbots that need fast, context-driven responses.
Through storing and managing vector embeddings (numerical representations of data), these databases perform similarity searches critical for real-time AI tasks.
Google Cloud has integrated ScaNN technology into AlloyDB, which powers major Google services like Search and YouTube, to greatly improve database performance in AI applications.
pgvector on PostgreSQL is driving AI innovation, but there’s more to come
Developers widely favor PostgreSQL as the operational database of choice, especially with the rise of pgvector extensions that enable vector searches—making PostgreSQL an attractive option for AI developers—but challenges still remain in scaling these databases for larger workloads.
As workloads grow, older algorithms like HNSW can suffer from increased latencies and memory consumption, limiting their effectiveness in enterprise environments.
ScaNN delivers fast queries with less memory usage
The introduction of ScaNN into AlloyDB offers a greater performance boost over previous solutions like HNSW. ScaNN provides 4x faster vector queries and 8x faster index build times, all while using 3-4x less memory.
This performance boost is critical for enterprises that need to process large volumes of data efficiently. ScaNN can scale to handle over 1 billion vectors, delivering top-tier query performance in PostgreSQL environments.
Valkey gets vector search and ultra-low latency for massive datasets
Google Cloud has introduced vector search capabilities to Memorystore for Valkey, providing the ability to perform similarity searches on over 1 billion vectors with single-digit millisecond latency and over 99% recall.
This makes Memorystore for Valkey highly suitable for AI applications requiring ultra-fast response times, such as fraud detection systems, personalized content recommendations, and real-time customer support.
Firebase takes a leap forward with Data Connect and PostgreSQL integration
Google Cloud’s Firebase platform is adding Data Connect, a new backend-as-a-service that integrates with a fully managed PostgreSQL database powered by Cloud SQL—giving developers a more robust, scalable backend solution—helping them build more complex AI applications with a reliable data infrastructure.
Data Connect is expected to enter public preview later this year.
The generative AI market is exploding with huge growth ahead
The market for generative AI applications is projected to grow exponentially, from $6.2 billion in 2023 to $58.5 billion by 2028, reflecting a CAGR of 56%. Growth here is being driven by increasing demand across industries for AI solutions that can improve decision-making, refine customer experiences, and automate complex processes.
As AI continues to mature, more enterprises are expected to invest heavily in AI technologies to stay competitive.
