# How do I deploy Llama 4 Scout with Zilliz Cloud for enterprise RAG?

Source: https://zilliz.com/ai-faq/how-do-i-deploy-llama-4-scout-with-zilliz-cloud-for-enterprise-rag
Author: zilliz

Use Zilliz Cloud for vector storage and retrieval, integrate Scout via LangChain or LlamaIndex, and serve Scout locally or via API to complete your RAG stack.

Architecture: (1) documents upload to Zilliz Cloud → (2) embeddings computed → (3) vectors indexed by Zilliz automatically, (4) user query embeds and searches Zilliz Cloud (returns top-k vectors + metadata), (5) retrieved text + query sent to Scout → (6) Scout generates answer grounded in retrieved docs. LangChain and LlamaIndex automate the orchestration: define your retriever (Zilliz Cloud), define your generator (Scout), chain them together. Zilliz Cloud handles scaling: if you grow from 1M to 1B documents, Zilliz scales a search engine providerally without code changes. Scout's serverless or self-hosted deployment is orthogonal.

For enterprises, this stack is powerful: Zilliz Cloud eliminates database operations (backups, scaling, monitoring), Scout's open weights eliminate vendor lock-in and enable fine-tuning. Monitoring: track retrieval quality (precision of Zilliz results) separately from generation quality (Scout's answer accuracy). Use Zilliz Cloud's analytics to optimize embedding and retrieval; use Scout's inference logs to optimize generation. This separation of concerns is why enterprise teams prefer this architecture in 2026.

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**Related Resources**
- [Zilliz Cloud — Managed Vector Database](https://zilliz.com/what-is-zilliz-cloud) — fully managed infrastructure
- [Getting Started with LlamaIndex](https://zilliz.com/learn/getting-started-with-llamaindex) — LlamaIndex integration
- [Retrieval-Augmented Generation (RAG)](https://zilliz.com/learn/Retrieval-Augmented-Generation) — end-to-end RAG architecture