As established in OpsMatters’ previous AI articles, generative AI is changing the way digital experiences are created. With its ability to generate new content from existing data – whether that be images, music, or human-like text – GenAI has opened up new possibilities for developers and businesses alike. However, to make any GenAI-powered application run smoothly, you need a reliable database.

Why? GenAI handles a massive amount of data, all of which needs to be stored, retrieved, and manipulated efficiently. A traditional database, or one that uses rows and columns, would not be ideal in this case.

This is where MongoDB Atlas comes in. It’s a fully managed cloud database designed to handle modern applications, including those with AI technology. Think of it as a super-organized and extremely fast digital filing cabinet that can keep up with all the demands of GenAI.

Here’s a breakdown of how Atlas’s features are suitable for such apps, according to this long-form post on generative AI at MongoDB.

Storing Data as Documents

In MongoDB Atlas, data is stored as documents, which are similar to files stored on your computer. Each document can have its own unique structure and can include various types of information, like texts, numbers, or images. Since GenAI involves processing large amounts of varied data, the flexibility of documents makes it much easier to work with.

Consider an app that generates artwork based on user input. You may upload a photo of a landscape for reference, while another might submit a drawing of a person. Atlas lets you store each piece of artwork in its own document, along with any other information like user preferences or metadata. This keeps everything organized and accessible.

Searching Vectors

Vector search is an emerging technology that plays an important role in GenAI. Vectors are like coordinates that represent data in a multi-dimensional space. When you upload a picture of a cat, the image can be converted into a vector with various dimensions that stand for different features, such as color, texture, and shape.

As VentureBeat points out regarding vector search’s potential, a database that stores vectors allows GenAI models to quickly search based on similarity. For example, an ecommerce site with an AI recommendation engine can offer suggestions based on the visual similarity of products rather than strictly relying on keyword matches.

A vector database is usually a separate system you need to integrate with your main database, but MongoDB Atlas makes it so that you can create vectors within documents. This makes developers’ lives easier since it eliminates the need to juggle multiple databases.

Scaling and Securing Data

When dealing with GenAI, expect fluctuating and often a growing amount of data. Traditionally, when an app starts to handle more data, you need to shift to bigger or more servers. This is called scaling, and it can be a hassle to manage manually. Fortunately, MongoDB Atlas provides horizontal scaling, meaning your data can be distributed across multiple servers without any manual effort on your part.

Security is another key concern, and you can read all about it on our “Security” category page. Atlas offers advanced security features to ensure data protection, like encryption and fine-grained access controls. These give businesses the ability to lock down who can access, modify, or delete data, and from where.

Conclusion: Your Gateway to Efficient GenAI Apps

As more companies become data-oriented, according to Harvard Business Review’s survey report, having a reliable database becomes crucial. GenAI applications are naturally complex to build, and the process can be even more challenging without the right tools.

You can think of MongoDB Atlas as a one-stop shop for all your GenAI needs. Rather than being just a mere storage system, it has an entire ecosystem of tools and features designed for GenAI applications.

OpsMatters has more articles like this. If you’d like to learn more about databases and how they can help AI, you can check out our write-ups on the “Databases” category page.