Why are vector databases becoming more popular as the AI hype cycle peaks?

Vector databases have become incredibly popular, driven by the surge in startups in this space and the investments flowing in. This boom is largely due to the rise of large language models (LLMs) and the generative AI movement, which have together created a fertile environment for the growth of vector database technologies.

Traditional databases like Postgres or MySQL, designed for structured data, struggle with unstructured data such as images and social media posts. Unlike these traditional databases, vector databases handle data differently. They convert various data forms into numerical representations called vector embeddings. These embeddings help in capturing the essence and relationships of data points, making vector databases ideal for machine learning applications by enabling semantically similar data retrieval with ease.

The utility of vector databases extends significantly to LLMs like OpenAI's GPT-4. They enhance a chatbot's understanding of context in conversations by analyzing past similar interactions. Moreover, vector search plays a vital role in real-time applications like content recommendations on social networks or in e-commerce, by quickly fetching items similar to a user's search query. It also aids in reducing inaccuracies in LLM applications by incorporating comprehensive and relevant data into the search.

Vector databases have not only caught the attention of startups like Qdrant, which recently raised $28 million, but also of other companies that have collectively secured significant funding for their vector-related projects. The adoption of vector databases is on the rise, with companies transforming complex data into vector embeddings to support various applications, highlighting the increasing recognition of their value across different industries.

Despite the hype around vector databases and their clear benefits in certain scenarios, they are not a one-size-fits-all solution for every enterprise search need. Industry experts believe that while specialized vector databases will serve high-end AI applications with their tailored functionality, general-purpose databases are also integrating vector search capabilities to cater to the wider needs of their users. This situation mirrors past trends where new technologies were adopted broadly alongside existing solutions, suggesting a future where both specialized and general-purpose databases will likely evolve to include vector search functionalities to some extent.