Hands-On RAG for Production

Chapter 5. The RAG Platform

In Chapters 2 and 3, you learned all about do-it-yourself RAG—from the basic components of embedding models and vector databases to more advanced components like hybrid search, reranking, and hallucination detection. Chapter 4 explored why taking a DIY RAG application from a simple proof of concept to a fully production-ready deployment is often more complex than it initially appears, highlighted the key challenges involved in scaling RAG in a real-world environment, and suggested steps to take for a successful DIY deployment in production.

A “RAG platform” (also known as RAG-as-a-service or turnkey RAG) refers to a technology platform that implements most, if not all, of the RAG components behind a developer API. This abstracts away a lot of the complexity of building RAG, letting developers instead focus on the RAG application itself—what data should the responses be grounded in, and how the application integrates into your business or application flow.

In this chapter, we will cover what a RAG platform provides and how to choose one that best fits your needs. We’ll demonstrate the usage of such a platform with Vectara.

DIY Versus Platform RAG

When you build a DIY RAG stack, you have granular control over each component of the RAG pipeline. This includes selecting and configuring vector databases (e.g., Pinecone, Weaviate, Zilliz, Qdrant), hosting and serving any embedding model (e.g., Cohere’s Embed v4 or Qwen3-Embedding-0.6B), defining and implementing ...