(and why they can’t be used as your primary database)

If you’ve been noodling on how to thoughtfully incorporate AI into your product, you’ve probably heard the directive to “add a vector database.” They’re powerful tools and they unlock entirely new product experiences. Naturally, a question comes up pretty quickly:

Can we just use a vector database instead of a traditional one?

The short answer is no.

It can be tempting to consolidate infrastructure to save on cost or complexity, but using a vector database as your primary database will create more problems than it solves.

What Traditional Databases Are Actually Good At

Traditional databases like Postgres or MySQL are excellent at exact, structured, flat, queries. By “flat,” I mean questions such as finding a user by ID, fetching rows that match a specific field, updating records, or enforcing permissions and relationships.

For example, if you ask: “Give me the user with ID = 1234”

A relational database will return that user instantly or tell you they don’t exist. It’s deterministic in that you will always receive the same response.

What Vector Databases Do Differently

Vector databases live in a completely different lane. They don’t store data according to rows and columns in the traditional sense. Instead, they store embeddings, which are numerical representations of meaning.

If you store these three phrases:

• “I can’t log into my account”
• “Login isn’t working for me”
• “I’m locked out”

A traditional database sees three different entities. A vector database understands that they’re basically saying the same thing.

So if a user searches:

“I’m having trouble signing in”

A vector database can retrieve all three, even though none of them match word-for-word.

That distinction is what enables semantic search, recommendations, support bots, and Retrieval-Augmented Generation (RAG). Instead of pulling back a specific record, a vector database retrieves the most relevant chunks of information based on semantic closeness to a user’s query.

How Vector Databases Fit Into RAG (Retrieval Augmented Generation)

This distinction becomes especially important in Retrieval-Augmented Generation. A typical RAG flow looks like this:

Say a user asks: “How do refunds work?”

That question is converted into an embedding and sent to the vector database.

The vector database doesn’t look for a file called “refund_policy.md.” Instead, it looks for chunks of text that mean “refunds,” even if they’re phrased differently such as:

• “We issue refunds within 14 days”
• “Payments are returned to the original method”
• “Refund eligibility depends on usage”

Those relevant chunks are pulled back and added to the prompt. Only then does the LLM generate a response, grounded in your actual data instead of guessing.

The vector database basically like a librarian who understands concepts, not keywords.

Why Vector Databases Make a Bad Primary Database

Where things go wrong is when teams try to use a vector database as a system of record. They aren’t optimized for frequent transactional requests. Imagine trying to use a vector database to manage things like: authenticating users, managing billing state, enforce role based access, etc… using a similarity search engine.

That’s not what vector databases are built for.

Trying to use one as your primary database usually leads to higher costs, slower development, confusing data models, and subtle security issues that only show up later. Vector databases shine at semantic retrieval yet fall short if relied on as a source of truth.

So here’s the better way:

The winning architecture is not either-or. It’s both, clearly separated.

A traditional database should handle users, accounts, permissions, metadata, and application state. A vector database should handle embeddings, semantic retrieval, and contextual grounding for LLMs.

When these two work together, you get reliability and structure on one side, and flexibility and intelligence on the other.

A Few Risks to Keep in Mind

Just like setting up a traditional database, you’ll need to ensure you incorporate security guardrails when setting yours up. Take advantage of metadata filtering so that you don’t accidentally lead customer data. You also want to be mindful of costs here. Be intentional about what you store and think about when you’d actually consider removing information from the vector database.

—

The short of it: used thoughtfully, vector databases can unlock powerful experiences.