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 a

Why vector databases aren’t just databases

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2024-10-09 02:00:04

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.