How do you design a clustering system that guarantees backward compatibility across model versions?

Backward compatibility in clustering systems is ensured using versioned models, frozen centroids, and stable feature schemas.

In production ML systems, cluster definitions evolve over time. To maintain backward compatibility, older models must remain usable even after retraining. This is achieved by versioning centroids, storing model metadata, and ensuring feature schema consistency. Systems often run multiple model versions in parallel and route requests based on version tags. This prevents breaking downstream dependencies that rely on stable cluster IDs.