# How do I optimize Scout retrieval latency with Zilliz Cloud?

Source: https://zilliz.com/ai-faq/how-do-i-optimize-scout-retrieval-latency-with-zilliz-cloud
Author: zilliz

Use vector index selection in Zilliz (GPU-accelerated IVF/HNSW), batch queries, enable Scout quantization, and pipeline retrieval with generation for parallel efficiency.

Latency bottlenecks: (1) embedding query → Zilliz (100ms–1s), (2) vector search in Zilliz (50ms–2s depending on index), (3) Scout generation on retrieved context (varies with context size). Optimize each: (a) use Zilliz Cloud's GPU-accelerated indices (10–100x faster for large-scale search), (b) batch queries to amortize embedding overhead, (c) quantize Scout to int8 (reduces generation latency 2–3x), (d) pipeline: while Scout generates token N, pre-fetch metadata for token N+1. Zilliz Cloud's analytics dashboard shows retrieval time—monitor this separately from generation to identify bottlenecks.

For Scout's 10M-token processing, latency is dominated by generation, not retrieval. But Zilliz Cloud's sub-100ms retrieval means you can afford frequent re-queries in agentic loops without penalty. Use this: initial retrieval → Scout generates partial answer → agent decides to re-query Zilliz → retrieve more context → Scout refines answer. This iteration is fast because Zilliz is fast.

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**Related Resources**
- [Zilliz Cloud — Managed Vector Database](https://zilliz.com/what-is-zilliz-cloud) — high-speed retrieval
- [Retrieval-Augmented Generation (RAG)](https://zilliz.com/learn/Retrieval-Augmented-Generation) — latency optimization
- [Local Agentic RAG with LangGraph and Llama3](https://zilliz.com/blog/local-agentic-rag-with-langraph-and-llama3) — agentic iteration patterns