What is a RAG API? RAG as a service explained

Retrieval-augmented generation (RAG) connects language models to external or private data so they can produce answers grounded in retrieved information rather than training data alone, typically through ingestion and chunking, embedding, vector storage, retrieval, and generation with citations. Organizations can build self-hosted systems using tools such as FastAPI, LangChain or LlamaIndex, pgvector or Chroma, and local models through Ollama, gaining control over data and retrieval logic but taking on maintenance, parsing, access control, and upgrades. Managed RAG APIs from providers including Google Cloud, OpenAI, Cohere, and CustomGPT.ai operate parts of this pipeline, while RAG-as-a-service platforms such as Amazon Bedrock Knowledge Bases, Vectara, and Coveo add connectors, hybrid search, source citations, and permission synchronization. Because private document indexes reflect only their latest data sync, live web search can supplement them for current information; Exa presents its search API as a retrieval layer that returns query-relevant web passages with citations and filtering options. The choice between self-hosted and managed RAG depends largely on requirements for data control, customization, operational effort, connectors, permissions, reliability, and total storage, query, and model-token costs.

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