The honest answer is it depends — but that is not helpful when you are trying to budget. So here is the guide we wish every buyer had before their first call with a development partner: real market ranges, the drivers that move the number, and the levers that bring it down.
The short answer: typical market ranges in 2026
These are typical market ranges for custom AI work done by an experienced senior team, not a quote — your number depends on the drivers below. But if you are sanity-checking a budget or a proposal, this is the landscape:
| Project shape | Typical range | Timeline |
|---|---|---|
| AI strategy / feasibility sprint | $5,000 – $20,000 | 1–3 weeks |
| Prototype / proof of concept | $10,000 – $35,000 | 2–6 weeks |
| Production AI feature in an existing product | $30,000 – $100,000 | 6–12 weeks |
| AI MVP (new product, launch-ready) | $40,000 – $150,000 | 6–14 weeks |
| Computer vision system | $40,000 – $150,000+ | 8–16 weeks |
| Multi-workflow enterprise AI platform | $150,000 – $500,000+ | 4–12 months |
Two things worth noticing. First, the ranges overlap heavily — a tightly-scoped MVP can cost less than a sprawling “feature.” Scope discipline matters more than category. Second, the floor is real: teams promising a production AI system for $5,000 are either reselling a thin wrapper or planning to learn on your budget.
The three cost drivers
1. Scope
A single AI capability inside an existing app — summarise this document, answer questions over this knowledge base, flag these defects — is the cheapest shape because the product around it already exists. A new product means design, frontend, accounts, billing and infrastructure plus the AI. The most expensive projects are platforms that try to do several of these at once, which is why we usually talk teams out of exactly that.
2. Data
Clean, accessible data is cheap to work with. Messy, scattered or missing data adds discovery and engineering time — sometimes more than the AI work itself. Questions that move the price meaningfully: Is the data digital? Is it labelled (if the task needs labels)? Can we legally use it? Does it live in one system or seven? A useful rule: if you cannot point to where the data lives, budget for finding out — that is what a strategy sprint is for.
3. Production rigour
A weekend demo is cheap. Something accurate, monitored and safe enough for real users is where most of the real work lives: evaluation harnesses, guardrails against bad output, monitoring for drift and cost, rollback paths, security review. This is the gap between “it worked when we tried it” and “it works.” Skipping it does not remove the cost — it moves the cost to your launch week, with interest.
What each shape actually buys you
A strategy or feasibility sprint answers “should we build this at all?” — opportunity mapping, data readiness, a costed roadmap. It is the cheapest way to avoid the classic failure of spending six months on the wrong thing.
A prototype proves the risky part works: the model can actually read your documents, the accuracy is in a usable range, users understand the interaction. It is deliberately narrow — ugly is fine, wrong is not.
A production feature takes a proven idea and engineers it into your product properly: integration with your stack, evaluation, monitoring, handling the long tail of weird inputs. This is the most common engagement shape and the best value-per-dollar for most businesses.
An MVP is a launchable product: design, mobile or web build, the AI capability, and enough polish that real users will judge the idea rather than the rough edges. See AI MVP & product builds for how we keep these fast without making them fragile.
An enterprise platform — multiple workflows, several user roles, compliance requirements — is a different animal, and honestly, most companies should not start here. Start with the feature or MVP that proves value; platforms earn their budget with evidence.
The costs after launch
Custom AI is not a one-time purchase, and any partner who quotes it that way is hiding the second invoice:
- Inference costs — what the model charges per use. For most LLM apps this runs tens to hundreds of dollars per month at modest usage, scaling with volume. Model prices have fallen steadily, but usage grows to meet them.
- Monitoring and evaluation — someone (or something) has to notice when quality drifts. Budget it as part of the build, not an optional extra.
- Maintenance and improvement — models improve, APIs change, users surface new cases. A common planning figure is 15–20% of the build cost per year to keep the system healthy and take advantage of better models as they arrive.
Why quotes differ so much between vendors
Three legitimate reasons, and one bad one. Legitimate: seniority (two senior engineers usually beat six juniors on both cost and outcome), region (US/EU agency rates often run 1.5–3× those of strong remote-first teams — global delivery is why worldwide-remote studios like ours can price below Bay Area agencies for the same seniority), and what’s included (does the quote cover evaluation, monitoring and a handover, or just “the model”?). The bad reason: some quotes are demos priced as products. Ask every vendor the same question — “what happens in week two when it gives a wrong answer to a real customer?” — and the difference will be audible.
How to spend less (without cutting the corners that matter)
- Start with a prototype, not a platform. Prove value before you buy scale.
- Use retrieval before fine-tuning. RAG is usually the cheaper path to grounded, accurate answers — fine-tune only when it earns its keep.
- Buy the boring parts. Off-the-shelf auth, billing and hosting; custom only where you differentiate. (More on that trade-off in custom AI vs off-the-shelf.)
- Bring your data ready. Every hour your team spends locating and cleaning data is an hour you do not pay a development partner to do it.
- Define “done” as a number. “90% accuracy on this test set” is buildable; “it should be smart” is a blank cheque.
The cheapest AI project is still the one you don’t build because a two-week prototype showed it would not move the needle. That is a feature of the process, not a failure.
A budget-planning checklist
Before you ask anyone for a quote, write down: the workflow you want to improve, the data it depends on and where it lives, the number that defines success, who inside your company owns it, and the ceiling you are willing to invest to find out if it works. With those five things, any competent partner can give you a real range in one conversation — and you will be able to smell the vendors who cannot.
Want a ballpark for your idea? Tell us about it — we reply within one business day with an honest range, including “this does not need custom AI” when that is the truth.