TL;DR: A RAG POC that proves feasibility on your data runs roughly $10k–$25k over 2–6 weeks. A production-grade enterprise RAG system — with hybrid search, re-ranking, evaluation, access control and integrations — generally starts around $60k and rises with compliance and scale. Ongoing running costs (model API or hosting + vector DB) are separate and usually modest by comparison.
Building an enterprise RAG application typically costs from around $10,000–$25,000 for a proof-of-concept to $60,000+ for a production system with evaluation, security and integrations. The price is driven by data volume, accuracy requirements, compliance and how many systems it must connect to — not by a fixed rate card.
Note on figures: the ranges below are typical 2026 market estimates to help you budget. OpenMalo provides a firm, phased quote after a discovery call. This post is the cost companion to our RAG development guide.
What drives the cost of a RAG application?
Five factors move the number more than anything else:
- Data volume and messiness — clean Markdown is cheap; scanned PDFs, tables and mixed formats need more ingestion work.
- Accuracy and citation requirements — higher accuracy means more evaluation, re-ranking and testing.
- Compliance — HIPAA, PCI-DSS or self-hosted deployment adds engineering and review.
- Integrations — every system it connects to (CRM, support desk, intranet) adds scope.
- Scale and latency targets — high traffic and sub-second answers cost more to engineer.
What does a RAG project cost at each stage?
| Stage | Typical range | Timeline | What you get |
|---|---|---|---|
| POC | $10k–$25k | 2–6 weeks | Feasibility proof on a data subset, accuracy baseline |
| MVP | $25k–$60k | 8–12 weeks | Production-quality core, basic eval, one or two integrations |
| Enterprise production | $60k+ | 12–16+ weeks | Hybrid search, re-ranking, full evaluation, security, citations, monitoring |
What are the ongoing running costs?
Build cost is one-time; running cost is monthly. The main line items:
- LLM usage — foundation-model API calls, or GPU hosting if self-hosted.
- Vector database — managed vector DB or self-hosted storage.
- Embeddings & re-ranking — usually a small fraction of generation cost.
- Monitoring & maintenance — observability, re-indexing as content changes.
For most mid-sized deployments, running costs are far smaller than the build — but they scale with traffic, so design for cost from day one.
How do you keep RAG costs down without hurting quality?
- Start with a POC on a data subset — prove value before funding the full build.
- Right-size the model — use a smaller model for retrieval and reserve the large model for generation.
- Cache and batch — reuse answers to common questions instead of regenerating.
- Measure before optimizing — an evaluation harness tells you where quality (and spend) actually goes.
How much does it cost to develop an AI application in general?
RAG is one type of AI application; the same logic applies across the board. Cost depends on scope, integrations and compliance needs. A POC starts low to validate feasibility; MVPs and production systems are quoted after discovery. A trustworthy partner gives you a transparent, phased estimate with cost drivers broken out by team role and feature. See our pillar on what an AI development company does for the full engagement picture.
