How much does MVP design and development cost?

MVP development cost is the hours a first release takes times the rate of the people who design, build and test it. In 2026, the average hourly band for software development firms listed on the review platform Clutch is $25 to $49 in India, Ukraine, the Philippines, Mexico and Spain, $50 to $99 in the US and Poland, and $100 to $149 in Canada and Australia. Clutch puts the typical custom software project reviewed there at $10,000 to $49,999, with an average cost of about $132,000 and a usual length of about 13 months (Clutch software development pricing guide, September 2026).

Firms that sell MVP builds publish their own ranges, from under $10,000 for a basic version to $150,000 and beyond for a complex or regulated one; those are their prices, not measured market data. AI coding tools are changing the hours spent writing code, by amounts the studies disagree on. They are not changing the hours spent deciding what the first release must do, testing it with users or securing it. For a small team over three months, the worked example below comes to about $45,500 to $271,200, depending on the rate band.

The number on a quote tells you less than what it buys. We think the most expensive MVP is the one built twice: designed around the main path, launched, then rebuilt when the second release needs the roles, states and data the first never planned for. A first release should be the smallest product customers choose, built on decisions that still hold when the second release arrives. AI has made the screens and code cheaper to produce, so the money should move to the decisions. The rates come first below, then where we think the budget should go.

Market rates for an MVP team, 2026

Figures were checked in October 2026. A firm's rate already carries its management, unbilled time, tools and margin; a salary is pay alone. Federal ceilings are maximums, and orders can come in lower. Clutch's rates are what listed firms state and its project costs come from client reviews, on a platform that earns money by introducing buyers to those firms.

Ranges run so wide because "MVP" names a purpose, not a size. Eric Ries, who popularised the term, defined it in 2009 as the version of a product that brings the maximum amount of validated learning about customers with the least effort. The least effort depends on who has to use the product. When we designed the first version of Auxentios, a quoting product for heavy industry, interviews with eight users turned up six specialist roles, from sales representatives to pricing and maintenance experts, and the product was expected to pass 200 screens. We designed reusable components and layout rules first, so developers could assemble further screens from them (our Auxentios case study). Six roles is a different purchase from one, whatever the product is called.

What a first release costs at those rates

Turn the rates into a total with the team and months a quote implies. US wage data gives a floor for a US team paid at the median. Private employers spent about 30 per cent on top of wages in benefits (wages were 70.0 per cent of compensation in June 2026), so one full-time month (about 173 hours) costs an employer:

A worked example, for a hypothetical quote that implies two developers, one designer and half a tester for three months, about 1,820 hours in all:

The 70 per cent share is the private-industry average for all workers, so treat the floor as approximate. The use is a sense check. A quote far below the figure for its own team and months is not necessarily wrong, but one of five things, or a mix of them, explains it: a narrower scope, a lower-cost team, fewer people or months, a faster way of working, or work left out. The proposal should tell you which.

What AI changes about the hours

The measured studies of AI in software work do not all point the same way:

• Writing code: faster in some trials, not in others. In randomised field experiments at Microsoft, Accenture and a Fortune 100 company, developers given an AI code assistant completed 26 per cent more tasks (4,867 developers; June 2025), and less experienced developers gained more. METR measured experienced open-source developers taking 19 per cent longer with AI tools in early 2025; in February 2026 it said it is likely that developers are more sped up from AI tools now, while calling its newer data only very weak evidence of how much. In a randomised trial Anthropic published in January 2026, 52 mostly junior engineers learning an unfamiliar library with AI help were not significantly faster, and scored 50 per cent on a skills quiz against 67 per cent for those coding by hand.
• Security: not solved. In Veracode's tests of recent models on tasks with known vulnerability patterns, only 55 per cent of generation tasks produced secure code (March 2026).
• Stability: still a concern. Google's 2025 DORA research, from nearly 5,000 technology professionals, found that AI adoption now goes with higher delivery throughput but continues to have a negative relationship with software delivery stability.

So AI can shorten the production rows of an MVP budget, by an amount no study we found has measured for a whole first release. It does not shorten deciding what the first release leaves out, sitting with your users, security testing, or fixing what real users find. A quote written today may fairly carry fewer coding hours than one from two years ago. A quote that is low because "AI builds it", and shows no time for those, has left that work for later.

Where we think the money should go now

AI has moved the cheap part of an MVP. Producing screens and code takes less than it did; deciding what to build, and making it hold up when real customers arrive, takes what it always took. So we spend a first-release budget differently from a quote built on hours alone:

1. Decide what the release must prove before anyone prices screens. One promise, the people who must use it, and the assumption it tests. What waits for the second release is written down, so that release starts from a plan rather than a rebuild. Our guide to what a SaaS MVP must prove, and what to leave out sets out the method.
2. Design the states, not only the main path. Empty, pending, failed, not allowed. They are where a first release loses a customer's trust and, we would argue, where rebuilds begin.
3. Put the hardest interaction into working code early. Our design engineers prototype the riskiest moment in software before the build commits, so the team argues about something real instead of a picture.
4. Keep the decision attached to the work until launch. Design and engineering should build from the same written decision, and someone should check what ships against it. In our work that check is part of the job, because a design that drifts in the build is paid for twice.
5. Let AI produce, and keep people on the decisions. AI drafts screens, code and test cases; designers and engineers decide what stays. Where it saves production time, we spend that time on what it cannot do: sessions with users, security testing and the edge states.
6. Plan the weeks after launch. Real users find what no review did, so agree before you sign who fixes it and whether it is in the price.

We expect research to change the product, not only the plan. In our work on enterprise scheduling for Doodle, user interviews showed that the priority people give a meeting decides whether they make themselves available. That finding went into the product's object model and its Schedule Confidence calculator, and surfaced as colour-coded "bookability" scores (our Doodle case study).

What an MVP budget pays for

A first release has to let one meaningful task complete for real users, including when something goes wrong. The budget pays for every layer that task needs.

Running costs sit outside most build quotes: hosting, third-party services, email delivery and, if the product uses a language model, inference. Ask for them separately, because they continue after the build is paid for.

We price a defined first release on its agreed scope. Before we do, we map that scope across roles and access, the core object, the workflow, integrations, operations and the assumption the release has to test, so every row of this table is either in the price or written down as left out. That is how our SaaS and web application development work starts.

What moves the cost

Every driver works on one of the two numbers: scope changes the hours, the team changes the rate. AI can shorten the coding inside each driver; none of them goes away.

1. Number of roles. Each role brings its own views, permission rules and tests.
2. The core workflow's states. A record that can only be created and viewed is cheap. One that can be drafted, submitted, rejected, reassigned and restored needs design, logic and tests for every transition.
3. Integrations. Each connection brings its own authentication, data mapping, error handling and test environment, and its behaviour is outside your control.
4. Data import. Bringing in customers' existing records means cleaning and mapping data you did not design.
5. Compliance. If you process personal data of people in the EU, the GDPR requires data protection by design and by default (Article 25), which shapes the data model and access rules from the start.
6. AI features. Model providers bill per million input and output tokens, so the cost recurs and grows with use. And because, in OpenAI's words, "models sometimes produce different output from the same input", the feature needs an evaluation set and review work that ordinary features do not.
7. Platforms. A responsive web application is one product; native iOS and Android apps add more to build, test and release.
8. Team location and seniority. This changes the rate rather than the hours, and the table above shows it can move the total as much as any scope choice.

How to compare two MVP quotes

When two quotes for the same idea differ by a factor of three, make every assumption explicit before concluding that one team is more efficient. These are the questions we would want you to ask us, too.

1. Ask each bidder for an out-of-scope list. What a quote leaves out is easy to miss unless it is written down.
2. Map both onto the budget table above. Where one quote has a line and the other does not, ask whether the work is excluded or included elsewhere.
3. Count the roles, states and integrations each assumes. A low quote may assume one role, the main path and no failed payments.
4. Ask where AI tools are used and where the saved hours went. A credible answer names the coding they shorten and still shows time for decisions, review and security testing.
5. Ask how testing is done, and on what.
6. Ask whose accounts and code it is. Hosting, domains, app store listings and the code repository should be in your name.
7. Ask how a change is priced. A fixed price puts the risk of underestimating on the vendor, who prices that risk in; a time-and-materials quote shows a rate but not a total.
8. Ask who fixes what users find after launch, and for how long.

Once both quotes describe the same product, what remains of the gap is the rate, the way of working and the commercial model.

When a design and build studio is the wrong purchase

Not every first release needs a team like ours, and paying for one too early wastes money you will want later.

• You need a demo for investors next month. An AI app builder and a few determined weekends will get you something clickable for far less than any quote above. Spend on a full team once the demo has taught you something.
• You hold a tested specification and want the lowest rate for the hours. The decisions are made, so a development firm from the lowest band in the rates table is the rational purchase.
• It is an internal tool for one role, with no money, sensitive data or AI at stake. Your own engineers, working from a short brief, are the sensible choice.

A studio earns its cost when the first release has to become a business: several roles, money or personal data moving through it, AI people must be able to trust, and a second release already on the horizon. That is the work our studio is built around, and the honest way to judge any studio for it is a small, fixed-price first piece that shows you how it thinks.

Questions founders ask

How long does an MVP take? It is the months term in the sum. Development firms' own guides quote a few months for a simple first release and longer for multi-role or regulated products; we found no neutral dataset of MVP timelines. Clutch reports about 13 months for the custom software projects reviewed there, but those are not all first releases.

Can I build it myself with AI app builders? For a prototype that tests whether anyone wants the product, it can be enough. The cost then moves to what the prototype skipped: roles, failure states, security and data. Our guide to taking a vibe-coded app to production covers that step.

Does AI make an MVP cheaper? It can make the coding faster; the trials above disagree on how much, and none measures a whole first release. Decisions, user sessions, security testing and support take what they took. Ask any bidder which hours its AI use removes, and check that those are coding hours.