There is no single price for AI development — the cost depends on scope, seniority, and how you engage. This is a buyer's guide to the drivers behind the number, not a rate card, so you can read any quote you are given.
It explains the three things that move the price, how cost varies by engagement model, and why the cheapest hourly rate is rarely the lowest total cost — written by builders, not a sales desk.
Almost every difference between one AI quote and another comes back to these three. Understand them and you can compare any two proposals like for like.
The same problem can be priced several ways. The right one depends on how much you want to direct versus delegate — and how defined the work already is.
The lowest-cost entry point — a bounded test of whether an approach works, on a narrow slice. Cheap by design, but only valuable if it is scoped toward production from the start.
Billed per engineer per month or hourly. Cost scales with the number and seniority of people you embed — best when you have the process and need capacity. See AI staff augmentation.
A standing pod that owns a product area over time. Higher ongoing cost than ad-hoc help, but it buys continuity and accumulated context that reduce rework.
Priced against an outcome or an SLA rather than hours. You delegate a result; the vendor owns the how. Best when you would rather buy an outcome than manage a team.
Quoted to a defined deliverable and end date. Predictable when requirements are clear — and prone to costly change orders when they are still moving. See AI development services.
Commercials anchored to the return the work is meant to produce, agreed up front. Aligns price with value instead of with hours on a meter — how we prefer to work.
In the interest of disclosure: we publish this guide and Silicon Prime is one option you might price — so weigh what follows against the drivers above. We are a small, Stanford-rooted Responsible AI lab founded in 2011, and we are not the cheapest commodity headcount. If you need raw, low-cost hours for routine work, a larger generalist firm may price lower.
Where we fit, the economics favour senior leverage. The lowest hourly rate often ships slower, carries more defects, and leaves a system that is costly to run — and a large share of cheap AI work never reaches production at all, so the spend returns nothing. Our patent-pending Aegis AI process puts AI behind senior engineers to reach production faster and leave a more reliable system behind, which is where total cost of ownership actually drops.
Compare proposals on outcome and total cost — not on the hourly rate at the top of the page.
BJ's Restaurants, a 200+ location restaurant chain, runs a demanding production environment. Aegis AI supported the team with twice-weekly production releases and zero critical defects for the past year — the kind of reliability that lowers the real cost of software over time, not just the rate on the invoice. See the full Aegis AI proof.
The cost questions buyers ask before they shortlist — answered straight.
There is no single price, because cost is driven by three things: the scope and ambition of the work, the seniority of the people doing it, and the engagement model and location. A bounded proof of concept is a fraction of a full production system with monitoring and maintenance. The most useful number is not an hourly rate but a costed, ROI-backed proposal for your specific problem — what it will take, what it will return, and what is and is not included.
Three factors dominate. Scope: a narrow proof of concept costs far less than a production system that must run reliably, securely, and under monitoring. Seniority: senior engineers cost more per hour but usually deliver faster and with fewer defects, so they are often cheaper per outcome. Model and location: staff augmentation, dedicated teams, managed delivery, and fixed-scope projects price differently, and onshore, nearshore, and offshore each trade cost against collaboration. Data readiness and compliance requirements can move the number too.
Because the rate is not the cost. Junior or commodity teams often ship slower, carry more defects, and produce systems that are expensive to run and maintain — and a large share of cheap AI projects never reach production at all, so the spend returns nothing. Senior, AI-amplified teams cost more per hour but reach production faster and leave a more reliable system behind, which usually lowers total cost of ownership. Compare proposals on outcome and total cost, not hourly rate.
We start with a costed, ROI-backed proposal before any build: the scope, the projected return, and the commercials, with fixed scope and ROI-tied terms rather than an open-ended hourly meter. A senior, AI-amplified pod then runs against it, and you keep control at every checkpoint. Our patent-pending Aegis AI process is how we keep that fast and reliable — it backs a 200+ location enterprise on a twice-weekly release cadence with zero critical defects over twelve months.
A production system costs substantially more than a proof of concept because the work is fundamentally larger. A PoC tests one idea on a narrow slice; production must run reliably and securely for real users, with monitoring, error handling, security, and maintenance built in. The PoC is the cheap part — most of the spend lives in hardening it into something dependable. Scope a PoC toward production from the start so the early work is not thrown away.
Fixed-price suits work with clear, stable requirements and a defined deliverable; it gives predictability but invites costly change orders when the scope is still moving. Time-and-materials suits exploratory or evolving work where you want flexibility, but it puts the budget risk on you. AI projects often start exploratory and firm up over time — many buyers begin time-and-materials or with a bounded PoC, then move to a fixed or ROI-tied scope once the path is clear.
Launch is rarely the end of the spend. After go-live you pay to keep the system running and useful: inference and infrastructure to serve the model, monitoring and MLOps to catch drift and failures, and ongoing iteration as data and requirements change. These running costs compound over the system's life, so price the whole life rather than just the build — it is where AI either earns its keep or quietly costs more than it returns.
The costs that surprise buyers usually sit outside the headline build quote. Common ones include preparing and cleaning data, meeting security and compliance requirements, ongoing inference and infrastructure, monitoring and maintenance, change orders when requirements move, and the rework caused by cheap work that never reaches production. Ask any vendor exactly what is and is not included, and price total cost of ownership rather than the rate at the top of the proposal.
Reduce cost by narrowing scope, not by buying the cheapest hours. Start with a bounded proof of concept scoped toward production, get your data ready early, and define requirements tightly so you avoid expensive change orders. Senior, AI-amplified teams cost more per hour but reach production faster with fewer defects, which usually lowers the total bill. The biggest saving is not paying twice for cheap work that never ships.
Budget against the return the work is meant to produce, not against an hourly rate. Start from the business outcome, estimate what it is worth, and size the build and its running costs against that — including inference, monitoring, and maintenance over the system's life. The most useful artifact is a costed, ROI-backed proposal that states the scope, the projected return, and what is and is not included, so you can judge payback before you commit.
Tell us about the work. We'll give you a straight read on scope, the right engagement model, and a costed, ROI-backed proposal you can compare against the rest of your shortlist — before any build begins.
Thirty minutes. No pitch deck. We reply within 48 hours.