The Latency Myth: Why Reranking Is Still the Smartest Optimization You Can Make

The Latency Myth: Why Reranking Is Still the Smartest Optimization You Can Make

When teams start building retrieval systems, one of the first questions they ask is:

“Isn’t adding a reranker going to slow down my pipeline?”

The short answer is: yes, technically — but it’s a tradeoff that pays off massively.

The long answer is that reranking improves both efficiency and quality once you consider the full retrieval-generation loop.

Why Latency Alone Is a Bad Metric

Rerankers introduce an additional step in your retrieval pipeline. On paper, that sounds slower — and it is, by tens or hundreds of milliseconds.

But in practice, reranking lets you send far less irrelevant context to the downstream LLM or agent.

Instead of giving an LLM 50 low-quality documents (which increases both latency and token cost), you can give it 5 highly relevant, semantically optimized results.

That translates to shorter prompts, faster inference, and higher accuracy downstream — meaning the total end-to-end latency is often lower with a reranker.

In the era of agentic architectures, this matters even more. Every agent action may trigger multiple search passes — so doing fewer, higher-quality retrievals yields a better ROI than many shallow ones.

Our Latency Profile

Behind the ZeroEntropy API, our reranker achieves production-grade latency with realistic payloads.

In production deployments for a customer sending billions of tokens per day, we observe:

• p50: 75 ms

• p90: 125 ms

• p99: 238 ms

These are real-world latencies measured under live traffic — fully acceptable for both retrieval pipelines and AI agent loops.

How ZeroEntropy Compares

Benchmarking against leading rerankers:

ZeroEntropy’s zerank-1 delivers:

• ≈ 4 % higher accuracy (NDCG@10)

• ≈ 3.7× faster at small payloads

• ≈ 12× faster at large payloads

• and 2× cheaper than JinaAI

That’s not an incremental gain — it’s an order-of-magnitude improvement for real-time RAG and agentic workloads.

Self-Hosting Considerations

For teams building latency-sensitive systems, self-hosting is fully supported:

• zerank-1-small (1.7B) — Apache 2.0 open weights, easy to run on a single GPU

• zerank-1-xl (4B) — commercial license for on-prem or VPC deployment

Running these locally can cut network round-trip times entirely, bringing median inference to sub-100 ms — ideal for in-house retrieval stacks or compliance-constrained environments.

Takeaway

Adding a reranker does add a few milliseconds.

But giving your agent a smarter search step is still the fastest way to better performance.

In retrieval and agent loops, the real bottleneck isn’t “how long each search takes” — it’s how many times you have to redo it.

A better reranker means fewer passes, shorter contexts, faster responses, and higher accuracy — an unbeatable tradeoff.