Businesses are eager to leverage AI to open up new revenue streams and gain competitive advantage. While innovative new tools like large language models (LLMs) have become widely available, integrating them into the tech stack poses numerous challenges. Much like adding standalone search engines and time-series databases for full-text search and IoT use cases, adding AI capabilities will involve expanding database capabilities and, potentially, increasing tech sprawl and the complexity that comes with it.
In our white paper, Standalone Databases vs. the Modern Data Platform, we explore the different options available when adding purpose-built functionality, like vector search for AI use cases, full-text search, and time series data.
Read the white paper to learn:
The importance of vector search and vector databases for adding business context and accuracy to LLMs
How adding capabilities like semantic search, full-text search, and time series collections can lead to escalating cost and complexity
Why a platform approach where capabilities are supported natively can reduce complexity and improve developer productivity
