Ever browsed an online store and clicked on a jacket, only to be shown five others that “feel” similar - even if they’re not the same color, material, or brand?
That’s not keyword magic - that’s a vector database at work. Behind the scenes, each product is turned into a numerical representation (called an embedding) that captures its style, look, and features. A vector database stores all these embeddings and makes it easy to find similar ones - so when you click that jacket, it finds others that match in vibe, not just words.
Now imagine going beyond just similarity - what if you could also factor in relationships like the designer behind the product, its release collection, what other customers bought with it, or how it connects to a larger style trend?
Enter: the graph-based vector database.
Why Is Everyone Suddenly Talking About Graphs Again?
A few years ago, graph databases were largely a niche - mentioned mostly in academic circles or tucked away in discussions around social network analysis and recommendation systems. As one user in this Reddit thread put it:
“They sound good on paper, but no one wants to deal with the overhead.”
And honestly, that’s how a lot of engineers felt. Relational databases worked well enough for most use cases, and anything beyond that was considered overkill or “just too complex.”
However, the AI landscape is shifting.
With the rise of multimodal AI systems - models that consume and reason across text, images, video, audio, metadata - our infrastructure needs are changing. We’re no longer just indexing documents. We’re modeling relationships, context, and meaning across disparate modalities.
As the team at ApertureData put it: “You need a purpose-built database to handle the volume, variety, and velocity of multimodal data.” Traditional relational databases and even standard vector stores can start to feel like trying to build a puzzle with only half the pieces. You see fragments of the picture, but without the relationships between them, you're stuck guessing.
What Are Real Users Still Unsure About?
To better understand what’s driving both interest and hesitation around graph-based vector databases, I reached out to the team at ApertureData, a company building a multimodal-native database.
I asked on their Slack:
“What kind of doubts or uncertainties do you see in the industry regarding why one should use a graph-based vector DB? Or what's gravitating people to start using such a database?”
The responses were thoughtful and pointed out to common questions teams still have as they consider making the shift:
“How do I convert my existing data into a graph?”
“How do I even query for content in a graph structure?”
“Do I have enough data to justify this model?”
“My schema isn’t well-defined - is that a blocker or a reason to go graph?”
These beginner questions highlight how graph-based thinking still feels like a shift - not just technically, but mentally.
But the reasons people gave for leaning into graph-based systems were just as compelling:
“We have data connected by foreign keys, but we struggle with multi-hop queries.”
“Our schema keeps breaking as we discover new relationships in the data.”
“We're working with unstructured or semi-structured data - and it doesn’t fit neatly anywhere.”
One ApertureData engineer summed it up well:
“Why do we need a ‘special’ database? Because JOINs weren’t designed for the types of questions we’re asking today.”
This isn't just about speed; it's about how easily you can represent relationships and context as your data and use cases grow more complex.
So, What’s Really Driving the Shift?
And that brings us to the bigger picture.
These aren’t isolated developer frustrations. They are symptoms of something larger happening in the industry - especially as companies move from experimenting with multimodal AI and agents to deploying them in production systems.
As laid out in SingleStore’s 2024 vector DB landscape, we’re watching a shift where vector-only isn’t enough.
Modern AI applications are:
Connecting vision, language, audio, and structured metadata
Reasoning across tools, timelines, and relationships
Evolving in complexity, requiring more adaptable retrieval patterns
A plain vector store can tell you what’s similar. A graph-based vector database can tell you what’s related, connected, and contextually meaningful.
So… Should You Use One?
If you're working on multimodal AI, dealing with connected data, or building systems where context matters as much as content; a graph-based vector database is no longer a niche option. It's becoming a practical, even necessary, choice.
That doesn’t mean it’s the right fit for every use case. If you're working with simple, flat data and don't need to model relationships or trace reasoning paths, a standard vector store might be just fine. But if you’re finding that JOINs are getting messy, schema updates are painful, and your retrieval results feel surface-level, it might be time to rethink your data backbone.
The database world isn’t just about rows and columns anymore. It’s about meaning, connections, and adaptability. And graph-based vector databases? They are starting to look less like a future bet, and more like the infrastructure GenAI actually needs.
