Direct answer: RAG architecture is the combination of embedding, index, retrieval, and refresh systems that feed a model; for 1M documents and 100k monthly queries budget $3k–$12k/month for vector storage and embeddings, expect 20–250ms median retrieval latency, and plan for caching to cut model calls by 50–80% to make the math work.

A failed RAG rollout is usually not a model problem. Pinecone, Qdrant, Weaviate, Milvus and FAISS all return vectors; the operational differences are where product breaks. If you measure only model accuracy, you'll miss the $/query, tail latency, and freshness constraints that ruin adoption for customers and internal users alike.

RAG architecture tradeoffs

Embedding cost is a predictable baseline. OpenAI-style embedding calls typically cost about $0.0004 per 1K tokens; embedding a 1M-document corpus with average 300 tokens per document therefore costs roughly $120–$200 in one-off compute if you use batched inference on a managed API or sized GPU workers. Re-embedding for freshness multiplies that cost linearly.

Vector storage economics scale nonlinearly. Hosted vector DBs such as Pinecone or managed Weaviate often price for storage plus CPU: a 1M-vector index usually runs between $200 and $2,000/month depending on replica count and dimensionality. Self-hosted FAISS or Milvus on EC2 shifts that to EC2 + EBS costs — typically $1,000–$4,000/month for a reliable multi-AZ cluster at that scale but gives you full control over eviction, sharding, and custom metrics.

Search latency shapes UX choices. ANN search over a 1M-vector index returns top-K results in 10–120ms on a tuned FAISS instance on CPU, and under 20ms on GPU-backed indexes. But total user-facing RAG latency also includes embedding time (50–300ms depending on model), rerank time (50–150ms for a cross-encoder), and model generation (100–1,200ms depending on model). If your interactive latency budget is 300ms, you need pre-embedding, an in-memory cache, and a lightweight rerank.

Freshness and ingestion are the hidden drivers of cost. Batch re-embedding a daily delta for 100k changed documents costs roughly $40/month in embedding API charges at $0.0004 per 1K tokens, plus pipeline costs: a modest Kafka or Kinesis stream with worker fleet typically costs $1k–$4k/month. For sub-minute freshness you must adopt streaming embedding workers and accept a $3k–$12k/month operational footprint.

Recall, precision, and chunking are coupled decisions. Larger chunks (800–1,200 tokens) reduce index size but increase model token consumption and lower recall@k for fine-grained answers. Smaller chunks (150–300 tokens) increase recall@10 from roughly 70% to 88–95% on many doc corpora but raise index size and embedding cost by 2–4×. Your acceptable recall@k threshold should drive chunk size choice.

Reranking strategy is a cost/accuracy lever. A two-stage approach — cheap ANN retrieval then neural cross-encoder rerank — improves precision by 8–15 percentage points but adds $0.005–$0.03 per query for the reranker depending on model and batch efficiency. For high-value queries (SaaS billing pages, legal search) the added cost is justified; for broad search surfaces it isn't.

Treat retrieval as a product: caching, routing, and freshness policy determine whether RAG is a scalable feature or an exploding AWS bill.

What this means for a CTO

You must set three explicit thresholds before choosing tech: cost per 1k queries, freshness window, and latency budget. If your threshold is under $0.01/query, or you need sub-200ms end-to-end latency, buy a managed vector store and deploy a Redis hot-cache; if you require sub-second freshness for every document, plan on building a streaming embedding pipeline and higher monthly costs.

Make the build vs. buy decision with a 36-month TCO. A small team of five engineers costs roughly $900k–$1.1M/year fully loaded; a SaaS stack costing $3k–$15k/month will be cheaper for the first 12–24 months unless the product requires custom reranking logic, strict data residency, or latency under 150ms. Use vendor SaaS to decouple early product-market fit from infrastructure ownership, then re-evaluate at predictable volume inflection points.

Operationalize observability and SLOs immediately. Track three metrics per query: retrieval time (ms), recall@10 (percentage), and model calls avoided by cache (percentage). Achieving a 60–80% cache-hit rate with a 64GB Redis instance (roughly $200–$800/month) typically cuts model spend by 50–80% and flattens cost growth as queries scale.

Key actions and checklist

1. Benchmark end-to-end latency with your real queries and user devices before choosing infrastructure.

2. Run a 90-day cost projection: include embedding re-runs, vector storage, cache instances, and reranker calls; assume 2–3 re-embeds per month for active datasets.

3. Start with a managed vector DB and a Redis hot-cache for <100k monthly queries, and plan a migration window when index size or latency budgets demand self-hosted FAISS/GPU.

4. Treat chunking as a product decision: pilot with 2 chunk sizes, measure recall@10 and token spend, then lock a standard and document the tradeoffs.

5. Instrument reranker cost and use it only on high-value flows; default to lightweight lexical or dual-encoder re-rank for bulk traffic.

Three short vendor rules: choose Pinecone or Weaviate for fast time-to-market and SLA-backed ops; choose Qdrant or Milvus if you need more custom control over index internals; choose FAISS + GPU when single-digit-ms retrieval and maximum control are non-negotiable.

If you decide to build: allocate a 6–12 month runway for a reliable ingestion, embedding, and index pipeline with nightly full-index tests, incremental embedding, and eviction policies. If you buy, contractually define egress, snapshot, and export guarantees so you can move indexes without service interruption.

Finally, align product KPIs to retrieval quality not model perplexity. Revenue-relevant metrics are conversion lift, task completion time, and support-ticket reduction — measure those and map them to retrieval investments.

Actionable takeaways

1. Budget $3k–$12k/month for a 1M-doc RAG deployment and expect embedding re-runs and caching to dominate ongoing spend.

2. Use a Redis hot-cache to reduce model calls by 50–80% and preserve latency budgets under 300ms for interactive features.

3. Start with a managed vector DB until you cross predictable volume thresholds (typically 100k–500k queries/month), then evaluate FAISS/GPU for millisecond retrieval.

4. Make chunking, reranking, and freshness your primary knobs; tune them against recall@10 and $/query, not only model accuracy.

RAG architecture is not a pure ML decision; it's a systems and product decision. When you budget retrieval, you buy predictability — and predictability is what turns an impressive demo into a repeatable feature customers use every day.