Learn why vector databases, and particularly multimodel databases, have become such a popular component of AI-driven architectures.
As AI adoption accelerates, vector databases have become a cornerstone of modern AI infrastructure—especially with the widespread use of large language models (LLMs). These databases are designed to store and query vectors, which are dense numerical representations of data used by LLMs to understand, compare, and reason.
But their value goes well beyond simple storage. Vector databases often come equipped with built-in approximate nearest neighbor (ANN) algorithms, enabling highly efficient similarity searches between vectors—an essential capability in many AI-powered applications.
Understanding Vector Database Types
Broadly, there are two ways to handle vector data: through dedicated vector databases or by extending traditional databases with vector functionality.
Dedicated or “single-purpose” vector databases like Chroma, Pinecone, and Weaviate are optimized specifically for vector operations. While fast and specialized, they only handle vector data and require integration with other systems to manage additional data types.
In contrast, multimodel databases integrate vector support into existing platforms like PostgreSQL, MySQL, and Oracle Database. This allows organizations to manage and query vector data alongside relational or document-based data—all within one system.
Why Store Vectors in a Database?
By saving vectors directly in a database, teams avoid the need to regenerate them every time they’re needed, especially when applying LLMs to the same data repeatedly. Databases also offer powerful tools for managing vectors, such as indexing, compression, and partitioning—capabilities that raw file storage simply can’t match.
Vector databases aren’t just useful for similarity search. They also play a vital role in applications like recommendation systems, object recognition, and semantic search. Some solutions even allow direct integration with LLMs, letting the database handle vector generation from input data automatically—streamlining architecture and reducing the need for additional services.
One key use case is retrieval-augmented generation (RAG), where vector databases provide relevant contextual data to LLMs during response generation, greatly improving the quality and specificity of the output.
Multimodel Databases as a Launchpad for AI
Multimodel platforms offer a compelling advantage for companies entering the AI space. Since they build vector capabilities on top of existing infrastructure, there’s often no need for a new tech stack, complex data migration, or learning curve. A simple upgrade may be all that’s required to begin storing and querying vectors.
An added benefit is the ability to perform vector similarity searches directly within SQL queries—allowing traditional analytics and vector processing to work together seamlessly.
Here’s an example of a query that combines conventional SQL filtering with vector-based ranking:
SELECT picture, price, location
FROM houses_for_sale
WHERE price <= (SELECT budget FROM buyers WHERE buyer_id = :app_user_id)
AND location IN (SELECT preferred_location FROM buyer_locations WHERE buyer_id = :app_user_id)
ORDER BY VECTOR_DISTANCE(picture_vector, VECTOR_EMBEDDING(houses_llm USING :app_input_picture))
FETCH FIRST 10 ROWS ONLY;
This query filters listings based on the buyer’s budget and preferred locations, then ranks them by how visually similar each house is to a photo the buyer uploads. The VECTOR_EMBEDDING() function transforms the user’s picture into a vector using an embedded LLM. The VECTOR_DISTANCE() function compares that new vector against those stored in the database, ranking results by similarity.
Lowering the Barrier to AI
One major appeal of multimodel systems is accessibility. Developers already familiar with SQL can run advanced vector searches without having to dive deep into machine learning or understand vector math. In Oracle’s implementation, this multimodel design is part of a broader converged database approach, allowing users to apply familiar database features—like security policies, compression, and lifecycle management—to vector data as well.
Vector Databases Are Here to Stay
The growing need for smarter, faster, and more context-aware AI applications is driving widespread adoption of vector databases. Especially in the case of multimodel implementations, these systems offer a powerful, flexible way to bring vector search and LLM integration into existing data environments. By simplifying how vectors are stored, searched, and managed, vector databases are helping businesses unlock the full potential of AI—making them an essential part of the modern data stack.
