Enterprise RAG Reranking: Latency Cost vs Retrieval Quality

The reranker is the most tempting upgrade in retrieval-augmented generation: a second stage that re-reads the query and the candidates together, promising better answers for what looks like a config change. The promise is real — and so is the bill, which arrives as latency on every query and cost that scales with how many candidates you feed it. This page decides the stage properly: the families, the one variable that drives cost, and the test that says whether you need it at all.

The Families: What Reranks and How

Three families dominate the second stage: cross-encoders (query and passage read jointly, the strongest relevance signal per candidate, at cost linear in candidates and length), late-interaction models such as ColBERT (token-level embeddings precomputed per document, query-time scoring lightweight — quality between bi- and cross-encoders at far better latency), and LLM-based rerankers (a generative model judges candidates — capable, but shifting the burden from latency to cost per query); comparisons across a dozen production rerankers keep landing on these family boundaries rather than individual model heroics.

The family lens matters because models iterate quarterly while the scoring mechanics — joint read, precomputed tokens, or generative judgment — are stable engineering properties that decide your latency and cost curves.

The Tradeoffs: Candidates, Latency, and the $15K Lesson

The tradeoff concentrates in one variable — how many candidates the reranker sees: cross-encoder quality is real but linear in candidates, so reranking everything is the documented production failure (one startup reportedly spent fifteen thousand dollars a month reranking candidates it should have narrowed first), while the standard discipline reranks only the top-k from a cheap first stage; against that spend sits the quality gain, which is corpus-specific — measure it, because retrieval that already answers well gains little from a second opinion.

• The cost dial: candidate count — every additional candidate is another joint read at full length.
• The discipline: cheap first stage wide, reranker narrow — common practice feeds the top 10-50, not the top 500.
• The lesson: reranking everything is how rerankers end up costing more than the LLM they serve.
• The honest input: measured recall gain on your corpus — family comparisons agree the gain is query-mix specific.

None of this makes reranking a luxury: on noisy corpora with high-stakes answers, a cross-encoder over a narrow candidate set is among the highest-return changes a RAG pipeline can make. The discipline is measuring which side of that line your pipeline lives on.

The Conditional Verdict: When the Second Stage Pays

Rerank conditionally: the second stage pays when retrieval quality is the binding constraint — high-stakes answers, noisy corpora, first-stage recall visibly losing relevant documents — with cross-encoders on a narrowed top-k for quality-critical paths and late-interaction for high-volume paths where latency budgets bind; skip reranking when first-stage retrieval already surfaces the right documents (measure before assuming) or when the latency SLO cannot absorb tens of milliseconds more — and whatever ships, evaluate as recall gained per millisecond on your own queries, the metric the comparison literature itself recommends.

Infrastructure closes the loop for production paths: a reranker is one more model to host, and serving it on the same dedicated environment as your generation tier — with capacity that is reserved rather than burst-prayed — keeps the second stage's latency a design constant instead of a variable you discover at peak. Dedicated serving setups such as OneSource Cloud's make that a placement decision rather than a capacity negotiation.

FAQ

Do I need a reranker if I already use hybrid search?

Measure first, then decide: hybrid search fixes first-stage recall across lexical and semantic mismatches, but if your evaluation still shows relevant documents ranked below the cutoff, a reranker is the fix — while a pipeline whose first stage already surfaces the right documents gains little from a second opinion; the decision input is your own recall evaluation, not the architecture fashion.

Which reranker family should production use?

By latency budget and volume: quality-critical paths with room for tens of milliseconds take cross-encoders on a narrowed top-k; high-volume paths with tight budgets take late-interaction models, whose precomputed token scoring delivers most of the quality gain at a fraction of the latency; LLM-based rerankers earn their cost only where judgment quality is worth a per-query premium — production comparisons consistently land on this split.

How many candidates should go into the reranker?

As few as preserve the gain: rerankers score candidates linearly, so the count is the cost dial — common practice narrows to the top 10-50 from the first stage, and the right number falls out of a simple sweep measuring recall gained per millisecond (the metric reranker analyses themselves recommend) on your queries; reranking hundreds of candidates is the documented pattern behind production bills nobody meant to pay.