5 RAG Architectures Compared: Pros, Cons, Costs

"RAG" is now a spectrum, not a single pattern. Picking the right architecture saves you months of refactoring.

1. Naive RAG

Pattern: chunk → embed → retrieve top-k → stuff into prompt. Cost: $$ (cheap) Best for: simple Q&A over <10K docs. Failure mode: poor retrieval quality on technical/multi-hop questions.

2. Advanced RAG

Naive + reranking (Cohere Rerank) + query rewriting + metadata filtering. Cost: $$$ (Cohere adds ~$0.001/query) Best for: production RAG on 10K-1M docs. Failure mode: still struggles with multi-document synthesis.

3. Modular RAG

Pipeline of swappable modules (rewriter → router → retriever → reranker → synthesizer). Each module is a separate Chain. Cost: $$$ Best for: teams that need per-query routing (FAQ → fast path, complex → slow path). Failure mode: more components = more failure points.

4. Agentic RAG

LLM agent decides when and how to retrieve. May make multiple retrieval calls per question. Cost: $$$$ (4-10x naive RAG) Best for: research-style questions requiring multi-step reasoning. Failure mode: latency (10-30 seconds per answer), cost spirals.

5. Graph RAG

Build a knowledge graph from docs, query the graph for context. Cost: $$$$ (upfront graph construction is expensive) Best for: relationship-heavy questions ("who reports to whom"), enterprise knowledge graphs. Failure mode: brittle to schema drift.

Decision matrix

• Simple FAQ → Naive RAG, $20/month all-in
• Customer support over docs → Advanced RAG, $80/month
• Research assistant → Agentic RAG, $300/month
• Enterprise relationship search → Graph RAG, $500+/month

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