How do you design clustering systems with strict latency constraints (real-time inference clustering)?

Low-latency clustering systems precompute clusters and use fast nearest-centroid lookup at inference time.

Real-time systems cannot run full clustering algorithms during inference. Instead, clusters are precomputed offline and stored as centroids in memory or fast key-value stores. Incoming data points are assigned to nearest centroid using optimized vector search (e.g., ANN). Systems may also cache frequent assignments to reduce computation.