Generative AI applications have been gaining worldwide attention over the last few years, and demand for them continues to grow rapidly. Statista’s forecast reveals that the market size is expected to grow at an annual growth rate of 20.80% until 2030. This is also reflected in our roundup of the ‘Top 10 Fastest Growing Artificial Intelligence Companies to Watch’. Naturally, this growth comes with a surge of interest in the technologies that power generative AI.

One such technology is the vector database, which is integral to facilitating AI model training and operations. Vector databases are anticipated to enhance the capabilities of large language models (LLMs) further as developers explore more improvements.

What is a Vector Database?

Before discussing the future of vector databases, it’s vital to understand what they are and why they’re important.

A vector database is a specialized database that stores and processes data like vectors. In the context of AI, vectors represent feature descriptors that convert complex data (such as text, image, or sound) into numerical values. These features are then restyled into a high-dimensional space where data similarities can be calculated.

MongoDB’s look at vector databases explains this further with an example. In a collection of photos, each image is a piece of data. An image has various features, like color, size, and texture. Each feature is then made into a vector (represented by numbers), and is arranged in a specific spot in a multi-dimensional space.

Such arrangement makes it easier to identify similar images by calculating the neighboring vectors. For instance, two photos with entirely different subjects may be deemed similar because of shared features like color patterns.

Advantages of a Vector Database

The future of vector databases is tied to the growing demand for efficient methods to handle large volumes of data for AI applications. They provide several advantages over traditional databases, including:

High-Speed Search

The design of vector databases makes it possible to conduct efficient, high-speed searches despite handling substantial volumes of data. Their specialty lies in similarity search, which enables more meaningful search results.

This is invaluable in the ecommerce industry, where, according to Google Cloud’s research on search abandonment, online retailers lose $300 billion each year due to bad online search experiences. With vector search enabled, customers can quickly find items similar to what they seek.

 Powerful Recommendations

Vectors play a critical role in producing tailored recommendations, a feature that has become increasingly desired by users across various industries, including entertainment. By using vectors to spot similarities based on user behavior and preferences, systems can make accurate recommendations.

For example, after a user listens to an alternative rock song by an American band, an app can suggest British indie music with similar chord progressions. This recommendation not only stays in line with the user’s musical taste, but also introduces them to new artists or genres, hence enhancing their user experience.

Naturally, this further enables businesses to gain more meaningful insights from their target market, something we explored in detail in our ‘Driving Value From Data’ post.

Relevant and Accurate Generative AI Results

LLMs can create new data instances similar to their training data, but they’re not always perfect in figuring out various hidden data patterns. This is why tools like ChatGPT, although remarkable, don’t always generate accurate facts.

Vector databases can help a machine learning model balance between exploring new patterns and exploiting known patterns more precisely. For instance, an image generator can create more detailed and accurate images by understanding high-level features and their relation to one another.

Better Threat Detection

As discussed on our previous post on ‘The Future of Cybersecurity’, digital transformation always comes with a wider threat landscape. Thus, if businesses leave vulnerable areas unchecked, the consequences can be unimaginably devastating. Investing in advanced security tools is now a chief concern for most businesses.

Since they have extreme classification capabilities to identify, count, and measure similarities between thousands of features, vector databases can also power cybersecurity tools. This technology can be used for things like real-time fraud detection and early threat identification, which are paramount in mitigating risks. As mentioned in the ‘The Future of Data is Real Time’, companies like Mastercard and Barracuda Networks are already implementing similar security measures.

Vector Databases in Small and Medium-sized Businesses

Vector databases can provide unique solutions for small and medium-sized businesses (SMBs) looking to leverage complex algorithms for competitive advantage. From personalized recommendations to predictive threat intelligence, vector databases can stay on par with the performance of much larger companies with extensive resources.

Their accessibility is reflected in the transparent and flexible pricing provided by several database vendors today. Due to their practicality and scalability, we are likely to see a wider usage in SMBs in the near future.