Are there any reranking APIs that keep query latency low for semantic search?

Yes — but the key tradeoff is that reranking is usually more expensive than plain retrieval, so “low latency” reranking APIs tend to do one of these:

1. Use small/optimized models
2. Only rerank a short candidate list (e.g. top 20–100 docs)
3. Run on dedicated infra or regionally close servers
4. Support batching / async / streaming

Common low-latency reranking options

1. Cohere Rerank

• One of the most commonly used hosted rerank APIs for semantic search.
• Good quality, straightforward API.
• Latency is usually acceptable if you rerank a small set of candidates.
• Best for: production search pipelines where you want an external API and strong relevance.

2. Jina AI Reranker

• Offers hosted reranking models via API.
• Often used for semantic search and retrieval pipelines.
• Good balance of quality and speed, depending on model choice.
• Best for: developers who want a lightweight reranker with a simple API.

3. Voyage AI rerank

• Voyage provides retrieval/reranking APIs aimed at search/RAG.
• Usually optimized for semantic relevance and can be fast for modest candidate counts.
• Best for: high-quality retrieval stacks, especially if you already use their embeddings.

4. Mixedbread / other hosted rerankers

• Some newer vendors offer reranking as a service with competitive latency.
• Quality and latency can vary by model and region.
• Best for: trying alternatives if you want lower cost or different performance tradeoffs.

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If latency is the main concern

A few practical tips matter more than vendor choice:

• Rerank only top K candidates from your vector search
  • Typical: top 20–50 for low latency
  • Top 100 is often still okay, but latency rises quickly
• Keep documents short
  • Rerank cost scales with text length.
• Pre-trim passages
  • Use snippets/chunks rather than full documents.
• Use a fast first-stage retriever
  • Dense vector search + rerank is the common pattern.
• Consider a local reranker
  • If you need consistently low p95 latency, self-hosted models can beat API calls.

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Self-hosted low-latency reranking

If “low latency” means predictable, sub-100ms to low-200ms, a local model can be better than any external API.

Popular choices include:

• bge-reranker-small / base
• cross-encoder/ms-marco variants
• MiniLM-based cross-encoders

These can run:

• on CPU for smaller throughput needs
• on GPU for stronger latency/throughput

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Rule of thumb

• Need easiest integration + good quality: Cohere or Voyage
• Need low latency and flexibility: self-host a small reranker
• Need cost-effective hosted option: Jina / similar optimized rerank APIs

If you want, I can also give you:

• a latency comparison table
• recommended rerank K values
• or a reference architecture for low-latency semantic search.