“How much will our AI app cost?” is the first question we hear, and the honest answer is: it depends on a few specific decisions. This guide breaks down LLM app development costs so you can budget with confidence.
Typical build budgets (USD)
| Type of LLM app | Typical build | Timeline |
|---|---|---|
| Prototype / proof of concept | $5k – $12k | 2–4 weeks |
| RAG chatbot over your documents | $12k – $30k | 4–8 weeks |
| AI feature inside an existing product | $10k – $35k | 4–8 weeks |
| AI agent with several integrations | $20k – $60k | 6–12 weeks |
| AI-first SaaS product | $40k – $120k+ | 3–6 months |
These are indicative ranges for senior teams. For a tailored range in under a minute, try the project cost estimator. Testing an idea first? Our MVP development sprint has one fixed price.
What drives the price
- Data work: cleaning, structuring and connecting the information the AI relies on.
- Integrations: every system the app reads from or writes to (CRM, ERP, helpdesk).
- Accuracy requirements: higher stakes need more evaluation, guardrails and review flows.
- User interface: a chat widget is quick; a full product with dashboards takes longer.
- Security and compliance: access control, audit logs, data residency.
Running costs: what you'll pay every month
LLM usage is billed per token. For most business apps, model costs are cents per conversation, and a small or mid-sized deployment typically lands between tens and a few hundred dollars a month, plus hosting and a vector database if you use RAG.
How we keep AI running costs low
- Routing simple requests to smaller, cheaper models.
- Caching repeated answers and embeddings.
- Sending only the most relevant context, not whole documents.
- Setting per-user limits and monitoring spend in real time.
RAG or fine-tuning: which is cheaper?
For knowledge-heavy apps, RAG is usually cheaper to build and maintain. Read RAG vs fine-tuning for the full comparison.
