GenAI adopters are applying language models to their own domain-specific data to reduce governance risks, increase productivity, and gain competitive advantage. To do this, they must make semistructured data objects such as documents and images intelligible to language models (LMs)—a nuanced task that the vector database is optimized to handle. The vector database stores, organizes, and delivers these objects to GenAI applications and the multifaceted workflows they drive.

Data teams should evaluate vector database platforms—including pure-plays and suites—according to their ease of use, scalability and performance, range of functionality, ecosystem support, governance, and deployment flexibility. They can use vector databases (DBs) to support four primary use cases: LM fine-tuning for GenAI; retrieval-augmented generation for GenAI; machine-learning models; and natural language processing. To succeed in these endeavors, companies must invest in team skills and training, data preparation, data governance, and workflow orchestration.

Eckerson Group recommends that vector DB adopters get started with these two steps:

1. Be realistic. Few technology waves have sparked more hype than generative AI, with predictions ranging from unprecedented productivity gains to steep job losses and killer robots. Business and data leaders must ignore the hype and set realistic objectives for incrementally enhancing their business with GenAI. The vector DB can provide trusted, domain-specific inputs to help reduce risk, improve governance, and meet achievable GenAI goals.

2. Take a holistic approach. The vector DB plays a specialized role in your overall data environment. Your business, strategic initiatives, and even your GenAI initiatives still need other platforms, such as relational DBs, graph DBs, and data lakehouses. Whether you select a pure-play vector DB or DB suite, be sure to have your team integrate these platforms as part of a flexible environment that supports current and future initiatives.