The 30-second version
RAG (Retrieval Augmented Generation) is how you ground an LLM in your own data — the difference between "ChatGPT guessed" and "Claude answered using your internal docs with citations." Production RAG isn't just "embed documents in a vector DB" — it requires chunking strategy, hybrid retrieval, reranking, query rewriting, and an evaluation suite to know if changes help or hurt. Nexora's vetted RAG developers ship the whole stack. 14-day refund, escrow-protected.
What a RAG developer can build for you
- Document ingestion + chunking — PDF, DOCX, HTML, Markdown, Notion, Confluence, Slack, Google Drive
- Embedding generation — OpenAI text-embedding-3, Voyage-3, BGE, Cohere embed; multilingual support
- Vector store setup — Pinecone, Qdrant, Weaviate, Chroma, Supabase pgvector, raw Postgres + pgvector + HNSW
- Hybrid retrieval — semantic + BM25/keyword, with proper score fusion (RRF or convex combination)
- Reranking with Cohere Rerank, voyage-rerank, or fine-tuned cross-encoders — boosts precision 20-40%
- Query rewriting + HyDE — improves recall for short or ambiguous user queries
- Evaluation suite — Ragas, TruLens, LangSmith, golden datasets, LLM-as-judge metrics
- Production deployment — FastAPI / Modal, streaming responses, citation rendering, query caching
Pricing in 2026
| Tier | Price | Delivery | Includes |
|---|---|---|---|
| Basic | $800 – $2,000 | 1–2 weeks | Single source, basic chunking + embedding, vector store, simple retrieval, demo UI |
| Standard | $2,500 – $6,000 | 3–5 weeks | Multi-source, hybrid retrieval, reranking, query rewriting, eval suite, FastAPI deployment |
| Premium | $6,000 – $18,000 | 6–12 weeks | Production multi-tenant RAG with monitoring, A/B testing, citation rendering, caching, fine-tuned rerankers |
The honest RAG quality ladder in 2026
- Baseline (weekend project): chunked PDFs, OpenAI embeddings, cosine similarity, top-5 retrieval, stuff into GPT prompt. ~50-60% answer quality.
- Hybrid retrieval: add BM25 keyword search, fuse with semantic via RRF. ~65-72% quality.
- + Reranking: retrieve top-25 with cheap embeddings, rerank to top-5 with Cohere/Voyage. ~75-82% quality.
- + Query rewriting (HyDE / multi-query): handle short/ambiguous queries. ~80-87% quality.
- + Smart chunking + metadata filters: respect document structure, filter by date/source/tags. ~85-90% quality.
- + Eval-driven iteration: golden dataset, regression tests, A/B production tests. ~90-95% achievable.
How to hire — the 4-step process
- Write a brief with: data source types and volume, expected query volume, latency target, budget, model preference
- Get matched with up to 12 vetted RAG developers within 2 hours
- Pay through Nexora escrow — funds release only when you accept the delivery
- Test against your golden dataset — accept, request revisions, or open a dispute within 14 days
Build a RAG pipeline you can actually trust.
Browse vetted RAG developers with verified production deployments, hybrid retrieval experience, and eval-driven iteration. From $800 prototypes to $18,000 production multi-tenant systems.
Browse RAG experts →Frequently asked
How much does a RAG pipeline cost to build in 2026?
Single-source RAG with basic retrieval costs $800–2,000. Multi-source hybrid retrieval with reranking and evaluation runs $2,500–6,000. Production multi-tenant RAG with monitoring, A/B testing, and observability are $6,000–18,000+. Senior RAG engineers charge $100–250/hr.
How much does RAG cost to run per month?
Roughly $50–500/month for moderate traffic (10k queries/month). Embeddings: ~$10–50 one-time to embed 1M tokens. Vector storage: Pinecone free tier or $70/mo scaled; Supabase pgvector free; Chroma self-hosted free. LLM tokens: $0.001–0.03 per query depending on model. Reranking (Cohere): $0.001 per 1k tokens. Add $20–100/month for FastAPI hosting.
Which vector database should I use?
For prototyping: Chroma (free, in-memory). For Supabase users: pgvector (free, integrated). For managed simplicity: Pinecone Starter (free tier good enough for many). For self-hosted with serious scale: Qdrant (Rust, fast) or Weaviate (most features). For 100M+ vectors: pgvector with HNSW indexes or Qdrant clustered.
What's the difference between RAG and fine-tuning?
RAG retrieves relevant context at query time and feeds it to the LLM. Fine-tuning bakes knowledge into the model's weights. Use RAG for facts that change frequently, document Q&A, and when you need citations. Use fine-tuning for style, format, persona, or proprietary task patterns. They're complementary — most production systems use both.
How do I evaluate RAG quality?
Build a golden dataset of 100–500 query-answer pairs. Measure: retrieval precision/recall at k (did we fetch the right chunks?), answer faithfulness (does the answer match the context?), answer relevance (does it answer the question?), context precision/recall. Tools: Ragas, TruLens, LangSmith eval, custom LLM-as-judge. Without eval, you cannot tell if changes help or hurt.
How long does RAG take to build?
A basic RAG prototype (single document type, simple chunking, OpenAI embeddings, Chroma store): 1–2 weeks. Production RAG with hybrid retrieval, reranking, eval and deployment: 3–6 weeks. Multi-tenant RAG with monitoring and A/B infrastructure: 2–3 months. Cutting the eval suite to save time is the #1 reason production RAG underperforms.
Last updated: 2026-05-23. Need help scoping your RAG project? Talk to the Nexora team.