Kavin Xavier

When I first wrote “Vector databases: Shiny object syndrome and the case of a missing unicorn” in March 2024, the industry was awash in hype. Vector databases were positioned as the next big thing — a must-have infrastructure layer for the gen AI era. Billions of venture dollars flowed, developers rushed to integrate embeddings into their pipelines and analysts breathlessly tracked funding rounds for Pinecone, Weaviate, Chroma, Milvus and a dozen others.

Companies hate to admit it, but the road to production-level AI deployment is littered with proof of concepts (PoCs) that go nowhere, or failed projects that never deliver on their goals. In certain domains, there’s little tolerance for iteration, especially in something like life sciences, when the AI application is facilitating new treatments to markets or diagnosing diseases. Even slightly inaccurate analyses and assumptions early on can create sizable downstream drift in ways that can be concerning.