How much does an AI chatbot on your documents cost in 2026?

In short

• Market: a custom RAG is billed at 10 to 25 k€ (simple) to 25 to 70 k€ (advanced), + audit (3 to 12 k€) and recurring costs.
• What drives the price: redeveloping everything from scratch, the volume/complexity of access rights, and a poorly sized vector database.
• Two distinct items: the build (one-shot) and the operations (monthly). It's the operations that most often gets out of hand.
• Starting from an already-developed building block + an optimized stack (pgvector, Cloudflare) halves both: base from 2 640 € excl. tax, low operations.

The problem, concretely

"Why a 30 k€ quote for a chatbot when ChatGPT exists?" Fair question. The answer: a reliable enterprise chatbot is not a ChatGPT wrapper, it's a pipeline (ingestion, search, guardrails, access rights, monitoring) to build AND to run. And the two costs have nothing to do with each other.

The build cost

The French market ranges: 10,000 to 25,000 € for a simple scope, 25,000 to 70,000 € for a large corpus with optimizations (not counting an audit at 3 to 12 k€). What drives it up:

• Redeveloping everything from scratch: ingestion pipeline, hybrid search, reranking, anti-hallucination guardrails (see how to avoid hallucinations).
• The volume of documents and the complexity of access rights (multi-tenant, confidentiality).
• The integrations (your channel, your IT system).

The operating cost (the one people forget)

This is where many projects get out of hand. Three items:

• Embeddings: low (a few dozen €/month for most corpora).
• Vector database: 50 to 500 €/month depending on the managed service… or nearly zero if you use pgvector in a PostgreSQL you already have.
• LLM: variable depending on the number of requests. A lighter, well-confined model reduces the bill without sacrificing reliability.

The choice of database illustrates the gap: pgvector (in Postgres/Supabase) is free if your instance has CPU/RAM headroom, and holds up perfectly to a few million vectors (HNSW). Beyond ~10-50 M vectors, or if vector search starves your transactional database, a dedicated database (Qdrant, Vectorize) becomes relevant. Choosing the right option from the start avoids a monthly bill that runs away.

Why starting from the existing halves the price (the nuance)

The core of a RAG (indexing, search, citations, anti-hallucination) is the same from one project to the next. By starting from a building block already developed and proven in production, you pay only for the customization to your business: the base is at a known price (from 2 640 € excl. tax), the rest is quoted as a flat fee. Honesty: if your need is very specific or at very high volume, full custom work still makes sense, but that's rarely the case.

Sources

• Price of an enterprise RAG assistant (market ranges)
• Supabase · pgvector performance (HNSW)