How Much Does It Cost to Build an AI App in 2026?

A production-ready AI app built by a specialist team typically costs $25,000–$60,000 (approximately £20,000–£48,000) and takes 6–10 weeks. Simple AI MVPs run $8,000–$20,000. Multi-agent or enterprise AI systems start at $60,000 and routinely exceed $500,000.

Those ranges look wide because "AI app" covers everything from a single chatbot to a company-wide automation platform. The table below shows what each tier buys.

AI MVP (2–6 weeks, $8K–$20K): one workflow automated, one model API, minimal integrations. Good for validating an idea before you commit.

Production AI app (6–10 weeks, $25K–$60K / £20K–£48K): multiple workflows, RAG over your data, auth, integrations with your existing systems, evals, and monitoring.

AI agent system (8–16 weeks, $40K–$120K): agents that plan and act across your tools — support automation, sales pipelines, document processing.

Enterprise AI platform (3–6 months, $60K–$500K+): multi-tenant SaaS, fine-tuned models, on-prem or VPC deployment, compliance (SOC 2, HIPAA, GDPR), and a full team.

1. AI complexity. A single LLM API call costs a few thousand dollars to integrate. A multi-step agent with tools, memory, and fallbacks is 3–5x more work because each step needs its own evaluation and error handling.

2. Data and RAG architecture. Grounding your AI in your own documents means building ingestion pipelines, a vector database, chunking strategies, and retrieval evals. This is usually 20–40% of the total build cost.

3. Integrations. Wiring into your CRM, ERP, or internal tools is where scope creeps. Each integration has its own auth, schema, and failure modes.

4. Who builds it. Hiring in-house costs 3–5x more than a specialist agency once salary, benefits, and the 3–6 month ramp-up are counted. A senior AI engineer costs $150K–$220K/year in the USA or UK before hiring costs.

Plan for $500–$3,000 per month on a typical production app: model API calls ($50–$10,000 by volume), cloud hosting ($20–$200), a vector database, and observability. Enterprise systems with heavy inference run $3,800–$30,000+ monthly.

Most of this is the model API. A well-architected system that self-hosts open-weight models or uses smaller models for routine tasks can cut inference costs by 50–80%.

Start with one high-value workflow, not five. Prototype on your real data for a fixed small scope — you will learn more in three weeks than in three months of planning.

Insist on owning the code and IP, and make sure evals and guardrails are in the scope from day one. Retrofitting them costs more than building them in.

Talk to a senior engineer about your specific use case. A free automation audit can tell you within a week which workflows are worth automating — and what they will cost.