How do you handle embedding model upgrades in production — what happens to your existing vectors when you switch models?

Difficulty: 7/10

embedding versioning, vector migration, index compatibility

Switching embedding models in production invalidates all existing vectors because embeddings from different models are incompatible; migration strategies include gradual rollover with dual-write and A/B testing, collection versioning, and background incremental re-embedding to avoid downtime.

When you switch embedding models, vectors generated by the old model and new model are not comparable—cosine similarity between them is meaningless[reference:12]. This is because each embedding model maps text into a different vector space. To migrate without downtime, use a phased approach: create a new collection/namespace for new embeddings, run dual-write to both old and new models for new/updated documents, and perform background re-indexing of existing documents. Gradual rollover over 1–2 weeks allows A/B testing and safe fallback[reference:13].

Embedding Migration with Dual-Write

Migration Best Practices

How do you handle embedding model upgrades in production — what happens to your existing vectors when you switch models?

Difficulty: 7/10

embedding versioning, vector migration, index compatibility

Switching embedding models in production invalidates all existing vectors because embeddings from different models are incompatible; migration strategies include gradual rollover with dual-write and A/B testing, collection versioning, and background incremental re-embedding to avoid downtime.

Embedding Migration with Dual-Write

Migration Best Practices

How do you handle embedding model upgrades in production — what happens to your existing vectors when you switch models?

Switching embedding models in production invalidates all existing vectors because embeddings from different models are incompatible; migration strategies include gradual rollover with dual-write and A/B testing, collection versioning, and background incremental re-embedding to avoid downtime.

How do you handle embedding model upgrades in production — what happens to your existing vectors when you switch models?

Switching embedding models in production invalidates all existing vectors because embeddings from different models are incompatible; migration strategies include gradual rollover with dual-write and A/B testing, collection versioning, and background incremental re-embedding to avoid downtime.

Scenario Questions

Follow-up Questions