“How much does AI cost?” is a fair question with an honest answer: it depends entirely on the problem. A focused automation and a company-wide platform are not the same project. Rather than quote a number that won’t fit your case, here’s what actually drives the cost — so you can budget realistically.
The phases you pay for
- Assessment. Mapping your workflows and data, then returning a prioritised roadmap. This is the smallest, lowest-risk spend — and it prevents the most expensive mistakes.
- Build. Designing, training and integrating the solution. This is usually the largest one-time cost, and it scales with complexity.
- Support. Monitoring, retraining and improving the model over time, typically on a monthly retainer.
What moves the price up or down
- Scope. One workflow costs far less than a platform touching many teams.
- Data readiness. Clean, accessible data is cheaper to work with than scattered or messy data that needs preparation first.
- Off-the-shelf vs custom. Building on existing AI models (for example, large language models via an API) is faster and cheaper than training a model from scratch — and it’s the right choice for most projects.
- Integration depth. A standalone tool is cheaper than one woven into your CRM, POS and internal systems — but deep integration is usually where the real value is.
- Accuracy and risk tolerance. Use cases that must be highly accurate or auditable need more testing and oversight.
Don’t forget running costs
Unlike a one-off website, AI has ongoing costs: the compute or API usage it runs on, and the support to keep it accurate as your data shifts. This isn’t a flaw — it’s the nature of software that learns. Budgeting for it from the start is what separates AI that keeps paying off from AI that quietly stops working.
How to get more value from your budget
The single biggest lever is starting narrow. A focused first project tied to one measurable outcome keeps cost and risk low, proves the value, and earns you a real number to justify the next step. Building on proven models rather than reinventing them, and integrating into systems you already use, stretches the budget further still.
Spend where it returns
The goal isn’t the cheapest AI — it’s the AI that returns more than it costs. We’ve been shipping AI inside real products for years (100+ products since 2015), so we scope projects around outcomes, not buzzwords. The honest way to get a real number for your business is a short AI assessment — and we’ll tell you plainly where AI is worth it and where it isn’t.