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.
Related Resources
- Zilliz Cloud — Managed Vector Database — fully managed infrastructure
- Getting Started with LlamaIndex — LlamaIndex integration
- Retrieval-Augmented Generation (RAG) — end-to-end RAG architecture