Vector Storage Workarounds

Relational databases without native vector support forced developers to choose poor in-database performance or a separate machine learning pipeline. Before native vector support, developers used BLOBs, JSON arrays, or external Python pipelines to store and process vectors. Storing vectors as BLOBs made meaningful querying impossible. Using JSON arrays made vectors more readable but required complex SQL and performed poorly for vector comparison. External Python pipelines worked functionally but created a two-system architecture with added API calls and latency.