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Vector databases are the wrong abstraction
Today’s vector databases disconnect embeddings from their source data. We should treat embeddings more like database indexes—here’s how.
We learned that while everything works smoothly for simple applications and PoCs, taking these AI systems into production reveals flawed abstractions with vector databases and the way we use them today. Thus, even in the best case, teams spend countless hours writing and debugging synchronization logic, setting up infrastructure to handle embedding updates at scale, and firefighting when these systems inevitably break down. The vectorizer abstraction simplifies this process by making it the system’s responsibility to manage these relationships, thus reducing the cognitive load on developers and minimizing the potential for mistakes.
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