The number one question every founder and CTO asks before starting an AI project: "How much is this going to cost?" The honest answer is: it depends — but "it depends" without context is useless. This guide gives you real, transparent pricing based on production projects delivered by Nxtr across 12+ countries.
The Three Cost Categories in AI Development
Before diving into numbers, it helps to understand the three distinct cost buckets in any AI project:
- Build cost — the one-time engineering cost to design, develop, and deploy the AI system
- Infrastructure cost — ongoing monthly cost for hosting (vector DB, compute, APIs)
- LLM API cost — the per-token cost of calling the AI model at inference time
Most pricing conversations focus only on the build cost. The infrastructure and API costs are equally important for unit economics — especially as you scale.
RAG Pipeline Development Cost
| Tier | Scope | Build Cost | Timeline |
|---|---|---|---|
| Basic | Single document type, one vector DB, GPT-4o integration, no custom UI | $15,000–$25,000 | 4–6 weeks |
| Mid-tier | Multi-source data, hybrid search, re-ranking, custom chat UI, API layer | $25,000–$50,000 | 6–10 weeks |
| Enterprise | Multi-tenant, metadata filtering, observability (LangSmith/Langfuse), CI/CD, SLA, security review | $50,000–$80,000+ | 10–16 weeks |
Infrastructure costs for RAG: vector database hosting (Pinecone Starter $70/mo → Enterprise $500+/mo), embedding generation ($0.02 per 1M tokens with text-embedding-3-small), and LLM API calls ($0.005–$0.06 per 1K output tokens with GPT-4o).
AI Agent Development Cost
| Tier | Scope | Build Cost | Timeline |
|---|---|---|---|
| Simple | 1–2 tools (web search, code execution), basic UI, no memory | $20,000–$35,000 | 4–6 weeks |
| Mid-tier | 3–5 tools, short/long-term memory, API integrations, custom frontend | $35,000–$65,000 | 6–12 weeks |
| Enterprise | Multi-agent architecture, HITL (human-in-the-loop), full observability, complex tool suite, enterprise auth | $80,000–$150,000+ | 12–20 weeks |
Model Fine-Tuning Cost
| Approach | Scope | Cost | Timeline |
|---|---|---|---|
| OpenAI Fine-tuning | GPT-4o mini or GPT-3.5 fine-tuning via OpenAI API; includes data prep | $10,000–$25,000 | 2–4 weeks |
| LoRA / QLoRA | Fine-tuning Llama 3, Mistral, or Phi-3 on your proprietary dataset; self-hosted | $15,000–$40,000 | 3–6 weeks |
| Full Fine-Tuning | Full parameter update on 7B–70B model; requires significant GPU compute | $30,000–$80,000+ | 4–10 weeks |
Full-Stack AI Application Cost
Many clients need the complete product: the AI backend (RAG + agents), the API layer, and a custom frontend. Full-stack AI applications with Nxtr range from $20,000 to $100,000+ depending on feature scope:
- AI SaaS MVP (AI core + basic web app, no auth, prototype quality): $20,000–$35,000, 6–8 weeks
- Production AI SaaS (AI core + polished web app, auth, billing, admin panel): $40,000–$80,000, 10–16 weeks
- Enterprise AI platform (multi-tenant, RBAC, custom integrations, security audit, deployment): $80,000–$150,000+, 16–24 weeks
Ongoing Monthly Retainers
Many clients work with Nxtr on a retainer basis for continuous AI development and iteration. Monthly retainers typically range from $8,000–$25,000/month depending on team size (1–3 engineers) and scope.
What Drives Cost? The 5 Main Variables
- Data complexity — how many data sources, formats, and update frequencies? Messy data that requires significant preprocessing adds cost.
- Integration depth — how many external APIs, databases, and systems need to connect to the AI layer?
- Custom tooling — off-the-shelf tools (web search, code execution) are cheap to integrate; custom tools that interact with your proprietary systems take more engineering time.
- Quality of evaluation — building rigorous evaluation pipelines (RAGAS, custom evals, regression testing) adds time but pays for itself by preventing production failures.
- Infrastructure requirements — multi-region, high availability, SOC 2, HIPAA, or GDPR compliance requirements add cost and time.
Build vs Buy vs Hire: The Real Trade-off
| Option | Upfront Cost | Ongoing Cost | Time to Launch | Customisation | Risk |
|---|---|---|---|---|---|
| SaaS AI tool (off-shelf) | $0 | $500–$5,000/mo | 1–2 weeks | Low | Vendor lock-in, limited IP |
| Hire in-house AI team | $0 (but recruiting cost) | $300k–$600k/yr per senior engineer | 3–6 months to hire + build | Full | Talent risk, slow to start |
| AI development agency (Nxtr) | $15k–$150k project | $200–$2,000/mo infra | 4–16 weeks | Full | Dependency on agency, but you own the IP |