RAG Pipeline Cost
RAG pipeline cost in 2025: indexing, retrieval, evals, and production grounding for LLM applications.
What drives Cost
Retrieval-augmented generation is the default architecture for grounded LLM apps, and pricing reflects whether you need a single-corpus demo or a production pipeline with evals, re-ranking, and freshness SLAs. A narrow-scope RAG runs fifteen to thirty-five thousand. Enterprise RAG with permissioning, multi-tenant isolation, and eval harnesses routinely lands above one hundred thousand. The ranges below reflect real 2025 builds.
- 01Corpus size and update frequency
- 02Chunking strategy and re-ranking needs
- 03Vector store choice (pgvector, Pinecone, Weaviate)
- 04Permissioning and row-level security
- 05Eval harness depth and regression coverage
- 06Latency budget and caching layers
Typical pricing tiers
- Chunking and embedding pipeline
- Single vector store
- Basic evals
- Simple chat UI
- Re-ranking
- Hybrid search
- Eval regression suite
- Observability hooks
- Row-level permissioning
- Multi-tenant isolation
- Freshness SLAs
- Compliance-aware logging
No surprise line items
Every engagement is scoped against a written statement of work. Changes are logged weekly and priced transparently. You always know where the number is going before it gets there.
A statement of work with deliverables, acceptance criteria, and a timeline before we start.
Every scope change is logged and priced within a week of being raised. No end-of-quarter surprises.
You own the code, prompts, weights, and infra-as-code. Standard work-for-hire clauses, no lock-in.
Runbooks, architecture diagrams, and a support retainer so your team can take it from here.
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Pricing questions
Do we need a vector database?
Not always. Postgres with pgvector handles most workloads up to a few million chunks. Dedicated vector DBs earn their keep at scale.
How do you measure RAG quality?
Golden-set evals, answer faithfulness checks, and retrieval precision at K. We track all three over time.
Can RAG replace fine-tuning?
Usually yes. Fine-tune when you need format control or domain tone, not for knowledge injection.
What about permissions?
Index-time filtering, row-level security, and user-context-aware retrieval. We do all three in production RAG.
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