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AI has the potential to discover things that humans might miss or overlook, thanks to its performance and consistency. But in the final analysis, the basis for developing AI is data.

That said, handling and protecting data appropriately is crucial. Data is not only the lifeblood of the entire IT infrastructure, but also the foundation of all innovation. As part of the generative AI infrastructure, databases are constantly evolving to meet the needs of enterprises in the generative AI era. The effectiveness of AI depends on how companies choose the right database to manage their data.

Common database models

Online Transaction Processing (OLTP) database is a database system architecture that supports transaction processing, allowing enterprises to process concurrent transactions performed by a large number of users at the same time, such as online banking transactions, online shopping, etc.

In addition, On-Line Analytical Processing (OLAP) technology can integrate transaction data and help integrate data from multiple sources, so it can conduct fast and in-depth interactive analysis of data from multiple dimensions. This technology greatly improves the analytical efficiency of enterprises. For example, a retailer could combine its inventory data with data on customer purchasing behavior to determine whether it needs to increase production of a particular item.

Emerging database models in the AI era

With the advent of the AI era, we expect the vector database model to be the most transformative.

Vector databases are used to cooperate with large language models to provide efficient data management required for "intelligent workloads" by converting unstructured data into high-dimensional vectors. It is estimated that by 2050, unstructured data including files, images, audio recordings, videos, etc. will account for 80% of global data. At that time, AI is expected to achieve higher-order semantic understanding, that is, understanding the underlying context and semantic nuances rather than pure literal meaning.

The ultimate goal of AI is to understand and leverage data, and vector databases are a key element in advancing industry-specific knowledge of large language models. For example, online gaming companies can use vector database solutions to create intelligent non-player characters (NPCs) that can interact with human players. These NPCs do not just talk according to preset scripts, but react based on real-time understanding of the content communicated by players, making the interaction more realistic.

Taking into account costs and benefits

Of course, the potential of AI goes beyond gaming applications, or even beyond understanding and processing unstructured data.

AI can also be used to manage databases. When storage space is low, AI can alert system administrators about storage needs and ask if storage space needs to be expanded. If authorized, AI can also automatically expand storage space. The same functionality applies to CPU capacity, storage capacity, and other features.

In the past, when purchasing cloud service products, a set of server resources had to be preset, but this would bring a certain cost. When the default server capacity exceeds the amount required by the actual workload, server resources will be wasted.

Serverless computing aims to solve this challenge by ensuring that the server capacity used by cloud services accurately matches the needs of the workload and can adapt as the workload changes dynamically.

By combining AI with serverless cloud computing, the advantages of both can be perfectly combined. AI can enhance enterprise decision-making capabilities to cope with sudden increases in demand or dynamically changing workloads. Enterprises also only need to pay according to the actual number of resources required, reducing usage costs.

Whether an enterprise can grasp the current AI trend and stand out from the competition depends on how to effectively utilize appropriate databases to fully realize the potential and advantages of AI.