How to Choose the Best Database for an AI App 

Developers

You’ve built something with an AI model, and now it needs access to your data. So where should that data live? Do you add a dedicated vector database, or use the one that already houses your documents, product details, and past conversations?

Picking wrong won’t necessarily break anything right away. But it might show up later. Either a second system to maintain, data that drifts out of sync, or a search feature that never quite performs the way you expected. This guide, written for app developers, walks through how to make that choice early, without getting lost in jargon.

TL;DR

• AI apps need a database that can store content and search it by meaning (vector search), not just by exact keywords.
• A second, dedicated vector database adds complexity: another system to pay for, secure, back up, and keep in sync.
• For most projects, especially smaller ones, a general-purpose database that also supports vector search is enough.
• MariaDB added vector search starting in version 11.8, so many teams can skip the second database entirely.
• A dedicated system still makes sense for very large or fast-growing content, heavy search traffic, or when search is the entire product.

An AI app usually asks three things of its database:

It has to store the content, whether that’s documents, product details, support tickets, or whatever the app should answer from. It has to search by meaning, known as vector search, where the app turns text into vectors, lists of numbers that represent meaning, so it can find the closest matches even when the exact words differ. And it still has to handle everything else: accounts, permissions, settings, and logs, the regular work any app’s database does.

You’ll come across two setups. A dedicated vector database is a system built specifically for storing vectors and searching them. A general-purpose database, the kind that holds your accounts, orders, and settings, can also offer vector search as one of its features. The rest of this guide covers which one fits your project.

Four questions to ask before you decide

1. How much content are you searching? A help center or product catalog is a different problem than millions of documents.
   
2. Is search the whole product, or one feature of it? A chatbot that also handles accounts and billing has different needs than a tool built entirely around search.
   
3. How many searches happen at once? Occasional lookups and constant, simultaneous queries put very different demands on a database.
   
4. Who’s going to run it? A second database needs someone to set it up, monitor it, and keep it secure.

Your answers to these four questions point toward one of the two paths below.

Why vector search is important

Picture a support chatbot for your product. A customer types “my invoice looks wrong.” But your help article is titled “Resolving billing discrepancies.” Because the word “invoice” doesn’t appear in the article, a plain keyword search may find nothing.

AI apps get around this with vector search. Similar meanings produce similar vectors, so the question and the billing article end up close together. The app finds the closest matches and passes that text to the AI model, which writes the answer. Your database has to store the content, run that search, and keep handling regular app data like accounts and permissions.

One database is often enough

Vector search sounds like it needs its own database. It often doesn’t, and a second database has costs that are easy to overlook.

It has to be set up, secured, backed up, and kept running. Your team has another tool to learn. And your content now lives in two places, so every change has to be copied across, or the AI app can answer from outdated information.

Say you’re running a product’s help center. A few thousand articles, moderate traffic, one small team. Adding a dedicated vector database here means a second system to secure and back up, just to search a collection that a regular database can handle without strain.

Or you’re building an internal tool that answers questions from a set of company documents. The content changes slowly, a handful of people use it, and the searches themselves are infrequent. A dedicated system would sit mostly idle while still needing someone to maintain it.

How MariaDB handles vector search

So can your regular database also do vector search? If it’s MariaDB, on recent versions the answer is yes. Vector support became generally available in version 11.8, the first long-term support release to include it.

You keep the vectors your AI model produces in a regular table, alongside the rest of your data. When your app runs a search, MariaDB compares the new vector to the ones already stored and returns the closest matches, the same idea as the invoice example earlier, just running inside the database you already have.

A vector index speeds up that comparison by returning approximate closest matches instead of checking every stored vector one by one, and MariaDB supports reads and writes happening at the same time, so your app can keep adding new content while people are searching.

You’ll find MariaDB 11.8 in the Kamatera marketplace, vector search included.

When a dedicated vector database makes sense

One cloud database isn’t the right answer for every project. A dedicated vector database earns its place when several of these apply:
Is your content very large or growing fast? A huge or fast-growing collection can strain a database built for general use, especially alongside all its other work.

Is search the product itself, not just a feature? If AI search is what customers pay for, its speed and quality justify dedicated tooling built around that one job.

Is search traffic heavy? Many searches happening at once can compete with the rest of your app for the same resources, slowing both down.

Does your current database actually lack the features you need? Check what it supports before assuming it falls short. Many general-purpose databases cover more than expected.

Do you have people to run it? A second system needs someone to set it up, monitor it, and keep it secure. Without that, it becomes a liability rather than an advantage.

If you answered yes to several of these, test a dedicated system against your current database using your own data.

Closing thoughts

Most AI apps, especially early on, are better served by keeping things in one place, and adding complexity only when the project actually needs it. If that changes later, a dedicated system is still there to add. Starting simple costs you far less than starting with a system you didn’t need.