SHORT ANSWER
An AI application costs roughly £10,000–£30,000 to build as a focused MVP on hosted models, and £50,000–£150,000 for a production system with retrieval, evaluation and integrations. Running cost matters more than build cost: expect £0.01–£0.30 of model spend per task. If a customer generates a hundred tasks a month, price accordingly — AI products fail on unit economics more often than on build budgets.
Answered by Andrew Crossley, Fractional Chief Product Officer · Updated 2026-08-01
Unlike traditional software, AI products have a real marginal cost per use. Flat-rate pricing over unlimited usage can turn growth into losses.
Teams underestimate evaluation and quality work, which is typically 20-30% of an AI build and is not optional.
Workflow and interface, model integration plus retrieval, and evaluation plus guardrails. The third is the one that gets dropped and then re-added expensively.
Tokens in and out, model tier, retries and context size. Multiply by realistic monthly usage per customer.
Usage-based, credit-based or tiered caps. Unlimited plans need a hard cost ceiling behind them.
Smaller models for easy cases, caching, prompt compression and retrieval trimming — validated against your evaluation set.
FROM EXPERIENCE
A £49/month product where the average customer runs 400 tasks at £0.08 per task spends £32 on inference alone. Gross margin before support is 35% — workable, but only if usage is capped and the model tier is right.
Same product at £0.25 per task is loss-making from the first customer. That calculation belongs in the business case, before the build starts.
Rarely at MVP stage. Prompting plus retrieval covers most needs; fine-tune when you have volume, stable tasks and evaluation data.
They can at scale, but add infrastructure and ops cost. Below serious volume, hosted frontier models are usually cheaper all-in.
Measure cost per successful task in the pilot and multiply by expected active users. Assume real usage is 1.5x your estimate.
IN SHORT
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MORE ANSWERS
No, but it is removing a large part of what product managers used to spend their week doing. AI already writes specs, summarises research, drafts tickets, analyses feedback and builds prototypes. What it cannot do is decide what a company should refuse to build, hold accountability for a metric, or persuade a room. Roles weighted towards documentation are shrinking; roles weighted towards judgement are becoming more valuable.
Startups use AI in three places: inside the product as a feature that completes a user task, inside operations to remove repeated manual work, and inside go-to-market for research, content and support triage. The highest-return use in a small company is usually operational — it lowers headcount pressure immediately, with no dependency on customers changing their behaviour.
Founders need four categories, not forty tools: a frontier chat assistant for thinking and drafting, an AI-assisted build tool for prototypes and MVPs, a research and synthesis tool for customer and market work, and automation for repeated operational tasks. Pick one per category, use them daily for a month, and only add a fifth tool when a specific recurring task justifies it.
AI reduces operating costs by removing repeated, rules-light manual work: support triage and first-draft responses, data entry and reconciliation, research and reporting, and QA. Realistic savings in a small company are 20-40% of the time spent on those specific tasks, not of total headcount cost. The saving is real only if the reclaimed hours are redeployed rather than absorbed.