Why Your Vector Database Should Be Treated Like a Cache

Embeddings are a build artifact, like a compiled binary. Change the embedding model and every vector you stored is invalid. The durable assets are the raw corpus and a reproducible pipeline—not the index.

• An embedding is derived data: a function of the source document, chunking strategy, embedding model, and model version.
• Upgrade the embedding model and every stored vector becomes stale. Different models define incompatible coordinate spaces.
• You cannot safely “normalize” old vectors into a new model. That creates silent retrieval drift, not a clean migration.
• The durable assets are the raw corpus and reproducible ingestion pipeline.
• The vector database should be treated as a rebuildable cache in front of that pipeline.

The Upgrade That Quietly Torched the Index

A model upgrade landed in an embedding stack. New version, better benchmark numbers, and a drop-in replacement on paper. Someone enabled it for ingestion and shipped.

Retrieval quality did not crash...

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