Vector Databases: What are they, and do you need one?

The short answer? Vector databases are amazing tools that power the modern internet—and yeah, you’re gonna probably want one.

But seriously, vector databases use mathematical models, strings of numbers, and algorithms to represent, store, and retrieve all kinds of data. And while there are certainly speed and performance gains to be had from leveraging vector databases, the real magic comes from building relationships between the data. This powers recommendations, nearest-neighbor search result capabilities, and conversational text generation. 

In other words, vector databases power artificial intelligence (AI) and the computers behind. Vector databases enable them to remember previous inputs and build contextual understanding of the data your users are interacting with.

Let’s take dive into how vector databases work, how they’re useful, and ways agencies and companies are deploying them. At the end, we’ll cover why a managed vector database may be a great option for your next project.

Vector databases vs a traditional databases

While an oversimplification, you can think of the difference between a traditional database and a vector database like 2D space vs. 3D space. It’s the difference between rows and columns of rigid, structured data vs multi-dimensional, unstructured data.

In a traditional database, data is organized into rows and columns. Finding the relationships between entries typically requires complex queries and filtering with little to no tolerance for deviations or errors. 

Think of keyword searching that is capable of only finding the exact search string entered. For instance, if you enter the search term “run” and search only able to find the exact text “run.” Traditional databases struggle to find alternate versions or interpret a misspelled word. In our example, it wouldn’t be able to return results for ran, running, or jogging nor understand misspellings like “rin”.

In vector databases, entries (which can be anything—text, images, video, etc.) are assigned numerical representations related to various characteristics. Entries are then clustered based on those vectors and the relative position with other entries. This enables simpler, common-language queries with a higher variance/error tolerance. So a search for “getting started rinning” while browsing running shoes might return content with run, running, jogging, etc.

The power of a managed vector database lies in its ability to cluster similarities in data. For instance, if your site has a vector database for songs, these songs could include dimensions for characteristics like artists, featured collaborators, release year, genre, subgenre, length in seconds, record label, whether it contains explicit lyrics, etc. If set up correctly, your vector database will cluster similar songs together for each of these dimensions. Returning recommendations based on one or more of these vectors just needs a mathematical algorithm measuring how closely they’re clustered.

Structured vs unstructured data

Okay. That was a lot; let’s make sure we’re all on the same page. First up: structured data vs. unstructured data. 

Think of structured data like a spreadsheet. The format and schema of the data are predefined and rigid. Each entry will have these exact data points, presented in a precise way. In other words, names are entered as text only, zip codes (in the US) as exactly five numbers, etc.

On the other hand, unstructured data does not have a predefined structure. In fact, it doesn’t require textual-only data, or even matching entry types for textual or non-textual data. 

For example, an unstructured database could include all of the following:

• Images in JPG, PNG, or GIF formats
• Audio in WAV and MP3 formats
• Videos in mp4 format
• Blog posts showing how to use products
• Social media posts reviewing the products
• Plus metadata for each multimedia entry in textual format

One of the problems computers have with unstructured data is being able to parse it and determine similarities. Enter vectors and vector embedding.

What are vectors? What is vector embedding?

Simply put, vectors are numbers and vector embedding is the process of converting unstructured data into vectors. This process typically requires subject matter expertise to set up correctly. 

Using our song database example, think about how closely related all the various subgenres of country music are to each other plus how close they might be to other genres like blues or rockabilly. Properly embedding requires baking in these types of relationships. Once an entry’s dimensions are vectors, a computer is better able to analyze them, determine similarities, and respond to queries.

Advantages of vector databases and do you really need one?

Vector databases deliver several significant advantages over traditional databases in the current digital landscape, including:

• Speed and performance
• Scalability and flexibility
• Real-time, dynamic updates

Let’s take a look at each one of those advantages.

Speed and performance

Because they’re accessed via algorithms, use a cluster-based model, and prize approximation over pinpoint accuracy, vector databases can deliver relevant results much faster than a traditional database, especially at scale.

Essentially, vector databases supply the much-needed context an AI/ML application needs. This avoids requiring it to re-parse an entire dataset for every query, so results are returned in milliseconds. This also reduces compute and storage needs compared to a traditional database.

Scalability and flexibility

Vector databases are built for expandability and adaptation—allowing the database and its use cases to evolve with business needs. Not only can it grow alongside your product/content/help libraries, it can adapt to new types of unstructured data. As your database grows, it can build new clusters to create additional context and more relevant results for users.

Compared to changes or additions to structured data which require rebuilding a traditional database, and the advantages are evident.

Real-time, dynamic updates

Content and data—yours and your users— is constantly changing. Since vector databases won’t require rebuilding or reparsing to accomodate these changes, they’re better able to respond to them. This means vector databases are better suited for use cases that incorporate regular updates or dynamic, user-based data. 

So the big question: Do your sites need vector databases?

Well, that depends. Do you want your users to have context-aware search capabilities on your site, to find what they’re looking for faster? Would robust and relevant product or content recommendations be helpful? Are support teams asking for bot-driven chat assistance to help answer user questions quickly? Do you want to build AI agent experiences into your site or software?

Assuming you answered yes to any of those questions, then yeah… you’re gonna need a vector database. But the real question is: Are you ready to deploy and maintain a vector database?

We’ve been talking about vector databases in simple terms. But the reality is that building, deploying, and managing vector databases is not for the faint of heart. They require optimized infrastructure, appropriate algorithms, regular maintenance, and updates to deliver on their promise of a faster, relevant, and more agile user experience. Which is why so many companies and agencies rely on managed vector databases.

Wait, what are managed vector databases?

As mentioned, building and maintaining a vector database is complex, expensive, and highly technical. Often agencies and brands need to focus their time and budget on other tasks, or simply lack the infrastructure and expertise needed internally. 

A managed vector database leverages a third-party to handle these complexities for you. WP Engine’s offering is designed to simplify AI app development for WordPress^Ⓡ by offering a comprehensive solution for data extraction, vectorization, and hosting, uniquely leveraging Smart Search indexing capabilities. 

Our solution offloads processing onto specialized servers, improving site outcomes while delivering:

• Automatic extraction, cleaning, and vectorization of data across your WordPress^Ⓡ site.
• Real-time, continuous indexing as site content changes.
• High-performance hosting of that data.
• Chatbot blueprint—a ready-made foundation for LLMs.
• Approximate Nearest Neighbor Search for highly relevant data retrieval.

In addition, flat-rate pricing added directly to your plan means predictable costs with no additional vendors or invoices to juggle.

WP Engine’s AI Toolkit

Our Managed Vector Database is available as a standalone add on and as part of WP Engine’s AI Toolkit. Our AI Toolkit also includes Smart Search AI with AI-powered Recommendations. Together, these tools supercharge your AI implementation, delivering improved performance and user experiences across the board.

Building AI solutions on WordPress^Ⓡ? Chat with our team about adding WP Engine’s Managed Vector Database service or AI Toolkit to your plan today!