Vector databases don’t just store your data. They find the most meaningful connections within it, driving insights and decisions at scale.
A vector database is just like any other database in that it stores data. From there, the similarity mostly ends — especially when it comes to artificial intelligence.
Most traditional databases are built for transactional workloads, where structured queries and relational data dominate. Vector databases, meanwhile, are all about unstructured data, built to support modern workloads like generative AI, machine learning inference, recommendations, and natural language processing. In fact, because vector databases focus on the unstructured, feature-rich vectors that AI systems feed off, they make these systems more like AI-driven search engines than databases as we’ve historically known them.
Indeed, with a vector database, you’re not just retrieving data, you’re retrieving the most relevant data. And that data is typically in the form of videos, audio, social media comments, community content, emails, web pages, and the like. Much like search engines, vector databases are designed to rank results based on similarity, custom scoring mechanisms, and other algorithms. This emphasis on relevance transforms the way data is accessed, based on how closely a result matches a query rather than just whether it fits an exact condition.
The benefits of vector databases come largely from their ability to perform approximate nearest neighbor (ANN) search. ANN search rapidly finds the closest vectors in high-dimensional space, enabling real-time similarity search over millions or even billions of records. Traditional databases, even when optimized with indexes, struggle with efficiently retrieving similar vectors.
To enable users to perform complex, comprehensive, multi-criteria searches, vector databases often blend vector search with traditional filtering capabilities. For example, a user might want to retrieve the most similar images in a vector collection, but only those that were uploaded in the past week or belong to a certain category. This hybrid approach — combining vector similarity with classic database querying — provides organizations with a powerful and flexible platform for building sophisticated AI-driven applications that leverage both the semantic understanding of vector embeddings and the precise matching of traditional database queries.
Vector database use cases
The use cases for vector databases include (but are certainly not limited to) advanced search, recommendation systems, data analysis, anomaly detection, and (especially critical to AI) retrieval-augmented generation, or RAG.
Used with large language models, RAG retrieves relevant information from a vector database to augment an LLM’s input, improving response accuracy, enabling organizations to safely leverage their own data with commercial LLMs, and reducing hallucinations. This enables developers to build more accurate, flexible, and context-aware AI applications, while offering a level of security, privacy, and governance when safeguards such as encryption and role-based access control are used with the database system.
Supporting AI at scale
Driven by the growing importance of vector search and similarity matching in AI applications, many traditional database vendors are adding vector search capabilities to their offerings. However, whether you’re building a recommendation engine or an image search platform, speed matters. Vector databases are optimized for real-time retrieval, allowing applications to provide instant recommendations, content suggestions, or search results. This capability goes beyond the typical strengths of databases — even with vector capabilities added on.
Some vector databases also are built to scale horizontally, which makes them capable of managing enormous collections of vectors distributed across multiple nodes. This scalability is essential for AI-driven applications, where vectors are generated at an enormous scale (for example, embeddings from deep learning models). With distributed searching capabilities, vector databases can handle large datasets just like search engines, ensuring low-latency retrieval even in massive, enterprise-scale environments.
Structured data still matters
All of this is not to say that structured data is not important. It is, as are the databases that are specifically built to store it. However, the majority of data generated today is unstructured, and organizations need a platform that can efficiently turn that data into meaningful insight. Working with volumes of rich unstructured data also enables AI systems to become “smarter” as they expand their ability to process and adapt to new and diverse scenarios. Vector databases can convert this complex data into vector representations that capture key features and semantic meaning, allowing AI models to work effectively with unstructured data at scale.
Your vector database isn’t just a database because it goes beyond the role of storing and querying data; it brings the power of real-time vector search, relevance ranking, and AI optimization into the fold. By combining the persistence and scalability of a database with the speed, accuracy, and ranking mechanisms of a search engine, vector databases are a fusion of both worlds, designed for the AI era.
Vector databases don’t just store your data. They find the most meaningful connections within it, driving insights and decisions at scale.
David Myriel is director of developer relations at Qdrant.
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