"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, OpenSearch, 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,""
Traditional databases rely on structured tables, schemas and exact-match queries, which fail to capture semantic similarity. Large language models and NLP applications require semantic retrieval over unstructured data, often using retrieval-augmented generation (RAG). Vector-native databases store and retrieve embeddings to enable high-speed, context-aware, multimodal similarity search for AI agents. Examples include Qdrant, Pinecone, OpenSearch, Weaviate and Chroma. Nearly 70% of engineers already use a vector database per an August 2025 HostingAdvice.com survey of 300 US engineers, and 73% of non-users are exploring one. Getting the most from vectorization often requires more legwork than simply adding vector types to legacy databases.
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