AI is turning the idea of a database on its head. Traditional databases, like relational and NoSQL databases, were designed for structured data and analytical queries—you have tidy tables, a schema, and clearly defined fields. Queries must match keywords and filters exactly, or else nothing is returned.
However, natural language processing applications powered by large language models (LLMs) are far looser. Instead of finding identical matches in structured records, these applications use techniques such as retrieval-augmented generation (RAG) to sift through massive amounts of unstructured data and find semantic similarities.
Enter the vector database. Vector databases are more dynamic than traditional databases, making them a good fit for AI use cases. New vector-native databases, like Qdrant, Pinecone, OpenSearchOpenSearch, Weaviate, and Chroma store and retrieve vector embeddings, enabling high-speed, context-aware, multi-modal data retrieval for AI agents, which is proving essential for RAG.
“Vector databases allow agentic AI systems to store and query massive amounts of unstructured embeddings—such as text or image features—with semantic similarity,” says Vagner Strapasson, tech lead AI engineer at Indicium, a data and AI consulting company.
As such, vector databases have become ubiquitous in the AI field. Nearly 70% of engineers are already using a vector database, according to an August 2025 survey conducted by HostingAdvice.com, which interviewed 300 US-based engineers holding roles in related data and AI and machine learning fields. Among those who aren’t using a vector database today, the majority (73%) are currently exploring one for future AI use cases.
But getting the most out of vectorization often requires more legwork than simply adding vectors as a new data type to pre-existing databases. Over three in four engineers say they are using a native vector database, as opposed to a traditional database with a vector add-on.
Below, we’ll explore how vector-native databases differ from traditional databases and weigh the benefits of going with a vector-native database versus using a relational or NoSQL database with vector storage support.
Interest in vector databases grows
The rise in interest around vector databases runs parallel to new requirements brought on by machine learning. As LLMs have made us all too aware, machine learning models can work with massive amounts of text or other unstructured data. However, machine learning models don’t work with this data directly, but with numerical representations of the data called vector embeddings.
These high-dimensional vector embeddings allow the semantic similarities between, say, words or paragraphs or other chunks of text to be measured in a vector space. The closer the vectors in this high-dimensional numerical space, the closer in meaning the corresponding chunks of text. Paired with approximate nearest neighbor (ANN) search, vector databases enable a quick, semantically-driven search and retrieval process, critical for working with generative AI and LLMs.
“In agentic AI applications, a vector database acts as external memory or a ‘knowledge index.’ It lets an AI agent recall relevant facts or past interactions,” says Indicium’s Strapasson. “Vector databases enable organizations to ingest, govern, and retrieve this coveted unstructured data—and then scale accurate, performant agentic AI systems,” adds Edward Calvesbert, vice president of IBM atsonxatsonx.
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