When should I use cross-encoder reranking vs. building a mixture-of-encoders system from scratch?

Use cross-encoder reranking as a quick win when you already have vector search deployed and need 20-30% relevance improvement - it's a plug-and-play solution that processes query-document pairs for better understanding. However, cross-encoders add 50-200ms latency per query and still inherit the fundamental limitations of your initial retrieval. Build a mixture-of-encoders system when:

1. your data has diverse attribute types (numerical, categorical, text, temporal),
2. users expect natural language to respect hard constraints ("under $2000" should never return $2001), or
3. you need consistent sub-50ms latency at scale.

The benchmark showed cross-encoders partially understood "5 guests" requirements but still returned wrong capacities in top results, while mixture-of-encoders achieved 100% constraint satisfaction. Implementation effort is 3-4x higher for mixture-of-encoders, but you get predictable performance across all query types rather than hoping the reranker fixes your retrieval mistakes.