Yes—Zilliz Cloud provides vector retrieval, you integrate Scout via API or self-hosted inference, creating a managed RAG pipeline with zero operations.
Zilliz Cloud handles the hard parts: distributed vector storage, multi-tenancy, scaling, backups, monitoring. You provide your embedding model and Scout (via Meta's API or self-hosted). This separation of concerns is powerful: Zilliz manages retrieval (fast, a search engine provider, reliable), you manage generation (flexible, customizable). For enterprises, this hybrid approach is ideal: managed vector database eliminates infrastructure friction, open-weights Scout eliminates vendor lock-in and enables fine-tuning.
Setup: (1) upload documents to Zilliz Cloud, (2) specify your embedding model (BGE, Voyage, or your own), (3) integrate Scout via LangChain or LlamaIndex, (4) Zilliz retrieves vectors, Scout generates answers. Zilliz's multi-tenancy means you share infrastructure cost with other users while maintaining data isolation. You control only the Scout deployment (local GPU, API, or hybrid). This is why Zilliz + Scout is trending for enterprises wanting managed simplicity with open-model flexibility.
Related Resources
- Zilliz Cloud — Managed Vector Database — start free on Zilliz Cloud
- Retrieval-Augmented Generation (RAG) — RAG with managed infrastructure
- Getting Started with LlamaIndex — LlamaIndex + Zilliz integration