In the age of generative AI (genAI), vector databases are becoming increasingly important. They provide a critical capability for storing and retrieving high-dimensional vector representations, essential for supporting large language models (LLMs). Unlike traditional databases that are optimized for exact matches, vector databases are designed to support similarity searches. Vector databases are ideal for applications where the goal is to find data points similar to a given vector. For example, a vector database can find images similar to a given image, or text similar to a given text. With vectors, LLMs can process requests quickly delivering the performance needed to run complex analyses.
Although vector databases have been around for decades, their application was limited. Forrester estimates the current adoption rate of vector databases at 6%, with a projected surge to 18% over the next 12 months. I believe the potential for vector databases is huge, especially to get insights from untapped data assets. We are already seeing organizations using vector databases to improve customer recommendations, for real-time anomaly detection with IoT data, and for fraud detection.
There Are Different Types Of Vector Databases
Besides storing vectors, vector databases offer several essential data management capabilities. These include efficient metadata storage, real-time data changes, granular access control, resource allocation for performance, concurrency management, and elastic scale. Vector databases have built-in search capability that quickly delivers optimized and relevant results, especially with complex data sets such as image, video, and audio. In addition, vector databases support pretrained embeddings of data such as word or image embeddings to provide fast access to support ML models. And its ability to store and process high-dimensional data efficiently allows it to find patterns and relationships invisible to non-vector databases.
Two types:
- Dedicated vector databases. These databases have an advantage over traditional databases when scaling to billions of vectors. They offer optimized storage and query capabilities for vector embeddings. Many organizations are using these databases for genAI, and we are hearing very positive feedback on their usage.
- Extended vector databases. These databases don’t support vectors natively but through vector indexes and functions. We believe that most traditional databases will offer some level of vector processing capabilities in the near future. Some traditional database vendors already support vector data, offering broader multimodel capabilities. Organizations are using them to integrate traditional structured and unstructured data with high-dimensional vectors to support semantically driven LLMs.
Originally posted on Forrester
