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What does AI developmentactually cost?

Answer six questions and get an honest range — build cost, monthly running cost, timeline, and the risks to vet your builder on. No email required to see the number.

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Estimated build cost

$40k–$80k

The production v1 ($50–100k class) · typically 10–16 weeks

Plus running costs

Roughly $800–$3,200/month at steady usage (inference, hosting, monitoring). Anyone quoting a build price without a monthly number is hiding this.

Your likely sink risks

  • Retrieval / answering from documents
  • Testing & evaluation
  • Data readiness

From the 8-discipline checklist in our free scoping pack — vet your builder hardest on these.

Illustrative ranges based on senior, vetted builders and real mid-market projects — every project differs. The number that matters comes from a 15-minute scoping call, and that’s free too: start a project brief.

The six factors that actually move the price

AI development pricing looks chaotic from the outside — the same one-paragraph idea can draw a $15,000 quote and a $150,000 quote in the same week. The variance isn’t dishonesty (usually); it’s that the paragraph doesn’t specify the six things that determine the real work. The estimator above is built on them, and they’re worth understanding before any vendor conversation.

  1. What the system is. A knowledge assistant answering from your documents is a different animal from a customer-facing feature, which is different again from multi-step automation that acts on its own. The stakes drive the engineering: customer-facing systems need deeper testing because mistakes are public; autonomous systems need containment because mistakes compound.
  2. Your data, as it actually is.The single most common schedule-killer. If the knowledge the system needs is clean and centralized, you’re fortunate and cheaper. If it’s scattered across formats and years of inconsistency — which is normal, not shameful — a data-preparation phase precedes the AI work. And if the knowledge lives mostly in people’s heads, your first project is capture, not intelligence.
  3. Integrations. Every system yours must read from or write to adds interface work, permissions, edge cases, and testing. One or two integrations is routine; three or more moves you into a different project class.
  4. Autonomy.“It drafts, a human approves” is the affordable and usually correct starting point. Every step toward unsupervised action multiplies the evaluation and safeguard work, because you’re no longer buying output — you’re buying trust.
  5. Compliance and sensitivity. Customer PII adds care; regulated domains add review, audit trails, and constraints on where data may flow. This cost is real and non-negotiable — a vendor who waves it off is a vendor to walk away from.
  6. Who maintains it. Not a build cost, but it shapes the build: a system handed to your own engineers must be boring enough for them to own, and a system the vendor maintains needs a support agreement priced honestly — typically 15–20% of build cost per year.

The three budget bands, honestly described

Nearly every successful mid-market AI project we’ve seen fits one of three bands. The pattern to notice: each band is defined less by what it includes than by what it deliberately excludes.

$25–50k buys the serious pilot. One workflow, one user group, real data, a genuine evaluation suite, and human review kept in the loop. The deliverable is a working system plus evidence— you exit knowing whether further investment is justified, which is worth more than the software itself. What it does not buy: integrations with everything, autonomy, or “the platform.” Vendors who promise those at this price are cutting the invisible parts — usually testing.

$50–100k buys the production v1.One core use case shipped to real users, with the unglamorous machinery that separates production from demo: monitoring, failure handling, handoff documentation, and your team trained to operate it. This is where most mid-market companies should land once a pilot — theirs or the market’s — has proven the problem.

$100–250k buys the integrated system. Multiple workflows, deep connection to systems of record, security review, staged rollout. Sane only when a pilot has already de-risked the core. Starting here without one is how companies buy $200k lessons.

Timeline follows the same honesty: 4–6 weeks for a pilot, 8–12 for a production v1, 16–24 staged for integrated systems. A vendor promising the big system on the small timeline is pricing in a discovery phase you’ll pay for later, with interest. Whatever the size, apply the two-week rule: something observable every two weeks. Real projects show working fragments early; troubled ones show slide decks.

Typical costs by project type

Ranges below assume mid-market scale, senior builders, and the mixed-quality data most companies actually have. Your factors move them — that’s what the estimator above is for — but they’re honest starting points.

  • Knowledge assistant / document Q&A — $30–80k. Answering questions from your policies, contracts, or documentation with sources. The cheap-looking version that skips retrieval evaluation is the one that confidently gives wrong answers; the difference between the ends of this range is mostly testing rigor and corpus messiness.
  • Document extraction and processing — $30–75k. Pulling structured data out of invoices, claims, applications, or contracts at volume. Highly sensitive to input consistency — standardized documents sit at the low end, decades of accumulated formats at the high end.
  • Internal workflow automation — $35–90k. Drafting, routing, summarizing, or triaging inside a process your team runs daily, with human approval kept in the loop. Cost scales with the number of systems touched more than with the AI itself.
  • Customer-facing AI app or feature — $50–120k. The public version of any of the above. The premium buys what public exposure demands: deeper evaluation, abuse resistance, brand-safe failure behavior, and monitoring that catches problems before your customers tweet them.
  • Agentic automation — $70–200k. Systems that plan and execute multi-step work with limited supervision. Wide range because scope discipline varies wildly; the successful ones start narrow, prove reliability per step, and expand autonomy on evidence. Be wary of anyone quoting the bottom of this range for the top of this ambition.

The costs nobody puts in the quote

Build cost is the number everyone negotiates; the numbers that surprise people arrive later. Three to demand up front:

Running costs. Every AI system has a monthly bill — model inference, hosting, monitoring — that scales with usage. As a planning figure, expect roughly 2–7% of build cost per month at steady state; a $60k system might run $1,200–3,000 monthly. This is knowable in advance. Make every vendor name their number and its assumptions, then hold them to explaining any later drift.

Data preparation.When it’s needed and unbudgeted, it emerges mid-project as a change order — the least fun way to buy it. If your honest answer about data is “it’s rough,” get the preparation phase scoped and priced separately, first. A builder who asks hard questions about your data before quoting is showing you competence, not caution.

Maintenance and evaluation upkeep.Models change, usage drifts, edge cases accumulate. Someone must own the system: watching quality metrics, updating content, deciding when a model upgrade is worth taking. In-house, that’s a slice of an engineer; outsourced, it’s a support agreement. Unowned, it’s a system that degrades quietly until an executive asks why nobody trusts it anymore.

How to spend less without getting less

The legitimate cost reductions are all decisions, not discounts:

  • Cut scope, not corners. The 20% of the ambition that serves 80% of the value is almost always identifiable in scoping — ship that, and let evidence argue for the rest.
  • Insist on boring architecture. Proven components, novelty only where it demonstrably earns its place. Exciting architecture is a cost center wearing a costume.
  • Keep humans in the loop longer. Draft-plus-approval delivers most of the value at a fraction of the safeguard cost. Let the system earn autonomy with a track record.
  • Phase it. Pilot → production → integration, each gated on evidence. Paying $40k to learn you shouldn’t spend $160k is excellent value.
  • Arrive scoped. A crisp brief with testable success criteria saves billable discovery weeks and attracts better builders — the good ones fight over well-scoped projects. Our free AI Project Scoping Pack is the template.

And one reduction that isn’t: hiring cheap. The market is full of confident generalists rebranded as AI experts, and the gap between them and the real thing doesn’t show in the demo — it shows in month three. Paying senior rates for a smaller scope beats paying junior rates for a bigger one, every time we’ve watched it play out.

AI development cost — common questions

How much does AI development cost for a mid-market company?

Most serious first projects land between $25,000 and $100,000. A focused pilot — one workflow, one user group, real data, an evaluation suite — typically runs $25–50k over 4–6 weeks. A production system for one core use case usually runs $50–100k over 8–12 weeks. Multi-workflow systems integrated with several systems of record run $100–250k. Quotes far below these ranges usually omit evaluation, data preparation, or maintenance — the parts that determine whether the system actually works.

Why do AI project quotes for the same idea vary so much?

Because vendors are quoting different projects. One is pricing a demo that works in a controlled walkthrough; another is pricing a production system with testing, failure handling, security review, and a maintenance handoff. The demo is 20% of the work. Before comparing quotes, make every vendor state what happens when the system is wrong, what it costs to run monthly, and who maintains it — the cheap quote usually gets expensive exactly there.

What are the ongoing monthly costs of an AI system?

Plan on roughly 2–7% of the build cost per month at steady usage, covering model inference, hosting, monitoring, and periodic quality checks. A $60k system might cost $1,200–3,000 a month to operate depending on volume and model choice. This number is knowable in advance — a builder who cannot estimate it has not thought about your system at production scale.

How long does an AI project take?

A well-scoped pilot ships in 4–6 weeks. A production v1 typically takes 8–12 weeks. Integrated, multi-workflow systems run 16–24 weeks, usually staged. Be suspicious in both directions: a two-week promise means a demo, and an open-ended timeline means the scoping was never done. Whatever the size, insist on something observable every two weeks.

Is it cheaper to use ChatGPT or an off-the-shelf tool instead?

Often, yes — and a trustworthy advisor will say so. If your need is individual productivity (drafting, summarizing, research), off-the-shelf tools at $20–60 per seat per month beat custom development. Custom work earns its cost when the system must use your data accurately, plug into your workflows and systems, or run a process at volume with quality controls. The honest test: if a generic tool gets you 80% of the value, start there and let real usage reveal whether the remaining 20% justifies a build.

Should we pay fixed price or time and materials?

For a first engagement, a fixed-price pilot with clearly stated acceptance criteria is the safest structure: you cap your downside, and the builder proves how they work. Time and materials becomes reasonable after trust is established, or for genuinely exploratory work — but pair it with the two-week observable-progress rule and a re-scoping checkpoint so honesty about surprises is rewarded rather than punished.

What makes AI development more expensive than it needs to be?

Three things, in order: fuzzy scoping (building the wrong thing precisely), messy data discovered late (the schedule-killer almost every project meets), and premature autonomy — letting the system act without human review before it has earned it. All three are avoidable before a contract is signed, which is why scoping discipline is the cheapest cost reduction available. Our free scoping pack exists for exactly this.

The real number comes from a real scoping call.

Fifteen minutes with us and your estimate becomes a scoped brief — then introductions to 2–3 vetted engineers or consultancies matched to your project’s actual risks. Matching costs you nothing.

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