Guide for buyers

Does AI Make Software Development Cheaper? What Buyers Should Know

By Obaid Tariq, Founder & Lead Engineer · Published 18 October 2026 · Updated 18 October 2026

Disclosure: AI Dev Planner is a software development agency that uses AI coding tools, so we have an interest in this question. We say below when we are not the right choice. See our disclosure policy.

The short answer

Partly. AI coding tools make some work faster, especially boilerplate, prototypes and test drafts, so a simple website or a demo can cost less than it used to. They do little for discovery, design decisions, integrations, security review, testing and operations, which is where much of a serious product’s cost sits. Expect savings on parts of a project, not a fraction of the whole price.

The research is mixed, and we cite it below. Already have something built with an AI tool? Start with the app readiness checker.

Will AI website builders replace web development agencies?

For some jobs, they already have. If you need a brochure site, a landing page to test demand, or a clickable prototype to show investors, an AI website builder or app builder can get you there in days for the cost of a subscription. Paying an agency for that work is harder to justify than it was a few years ago, and we would tell you so.

Builders stop being enough when the product has to do something specific to your business. Custom rules, integrations with the systems you already run, user accounts with different permissions, payments, personal data and a security posture you can defend are where generated apps tend to need the most work. The tools can produce code for all of these; the problem is knowing whether that code is correct, safe and maintainable, and having someone accountable when it is not.

So the realistic picture is not replacement but a new split: builders for the cheap, early and simple work, and developers for the parts that carry risk. Many projects now start in a builder and move to a team once they have users. If that is where you are, our vibe-coded app rescue service exists for exactly that handover.

AI website or app builder compared with a development team
Aspect	AI website or app builder	Development team or agency
Best for	Brochure sites, landing pages, prototypes and demos to test an idea	Products with custom rules, integrations, user data or payments
Custom business logic	Works for common patterns; harder to control as rules get specific	Designed around your actual workflow and its edge cases
Integrations	Usually limited to the connectors the platform offers	Any system with an API, including legacy ones, with failures handled
Data and access control	Depends on how the tool configured it; easy to get wrong without noticing	Data model and permissions designed and tested before launch
Security review	Not included; you need to arrange it	Should be part of delivery: ask to see how it is done
Accountability	The platform's terms of service; you own the outcome	A contract, a warranty and named people responsible for fixes
Upfront cost	Low: a subscription and your own time	Higher: you pay for people, scoping and testing

A general comparison: individual builders and agencies vary more than these categories do.

Should websites and apps be cheaper now because of AI coding tools?

Some of the work, yes. All of it, no. The honest answer depends on which part of a project you are looking at, and the research on how much faster developers really are does not agree with itself.

What the evidence says

Nearly everyone uses AI tools now. In the Stack Overflow Developer Survey 2025, 84% of respondents said they use or plan to use AI tools in their development process, and 51% of professional developers use them daily. Trust is lower: 46% said they distrust the accuracy of AI output, against 33% who trust it. The most common frustration, cited by 66%, was AI answers that are “almost right, but not quite”, and 45.2% said debugging AI-generated code is more time-consuming (Stack Overflow).
On a small, well-defined task, AI can be much faster. In a controlled experiment, developers with GitHub Copilot completed a task, writing an HTTP server in JavaScript, 55.8% faster than those without. The authors note that the study does not examine the effects of AI on code quality, and that benefits may vary across tasks and languages (Peng et al., arXiv).
On real work in large, familiar codebases, the picture is less clear. METR’s randomised trial with 16 experienced open-source developers working on 246 real issues found that, with early-2025 AI tools, tasks took 19% longer. The developers had expected a 24% speed-up, and afterwards still believed they had been sped up by 20%. METR says the result does not show that AI slows down most developers (METR, 2025). In February 2026 it said developers are likely more sped up now than in early 2025, but that its new data is only very weak evidence for the size of that change (METR, 2026 update).
Speed and stability move in different directions. Google’s 2025 DORA report, based on survey responses from nearly 5,000 technology professionals, found that 90% use AI at work and more than 80% believe it has increased their productivity. It found a positive relationship between AI adoption and software delivery throughput, but AI adoption “does continue to have a negative relationship with software delivery stability”; 30% reported little or no trust in AI-generated code (Google Cloud). The report describes AI’s main role as “an amplifier, magnifying an organization’s existing strengths and weaknesses” (DORA).

Two lessons for a buyer. First, how much faster developers feel is not good evidence of how much faster they are. Second, faster code without strong testing and review tends to produce more incidents later, and you pay for those too. All figures above accessed 18 October 2026.

Which parts of a project AI speeds up

Our editorial view of where AI tools change the effort in a typical project, consistent with the research above. These are directions, not measured percentages.

Parts of a software project and how AI coding tools affect the effort
Work	Effect of AI tools	Why
Boilerplate and standard screens	Often faster	Forms, CRUD screens and common patterns are well represented in what AI tools have learned from.
Prototypes and throwaway demos	Often much faster	Speed matters more than long-term quality, so the trade-off favours AI.
Unit tests and documentation drafts	Often faster	Drafts come quickly, but someone still has to check that the tests test the right thing.
Discovery and requirements	Little change	Deciding what to build means talking to you and your users. AI can summarise; it cannot decide.
Design and architecture decisions	Little change	Trade-offs about data, scale, cost and risk need someone who understands your business and is accountable for the choice.
Integrations with real systems	Mixed	Writing the call is quick; handling the other system's quirks, limits and failures is not.
Security review	More important, not less	More generated code means more code to review, and AI tools repeat insecure patterns.
QA, release and operations	Little change	Testing real user journeys, deploying safely, monitoring and fixing incidents still take people and time.

What this means for price: a brochure site or a prototype should cost less than it did, and you should push back if it does not. A product with real users, data and integrations costs less of a discount than the headlines suggest, because most of its work sits in the rows that do not change much. For reference, our own planning ranges for a robust MVP in the UK are £25,000–£45,000, internal tools run from £40,000 to well over £80,000, and customer portals start from around £60,000 and can exceed £120,000. The MVP cost calculator gives an indicative range for your own product.

Can a small agency deliver production-ready projects using AI coding assistants?

Yes, but only with the same guardrails any production code needs, applied more strictly. The risk is not that AI writes code; it is that generated code looks finished before it is. Veracode tested more than 100 large language models on 80 coding tasks and reported that the AI-generated code introduced security vulnerabilities in 45% of cases; on cross-site scripting, the models failed to secure the code in 86% of cases (Veracode, accessed 18 October 2026). That is a test of raw model output, not of code that went through review, which is exactly the point: review is what closes the gap.

The guardrails to look for

Human code review on every change. Someone other than the person who prompted the tool reads and approves it before it is merged.
Automated tests that run on every change. Especially around sign-in, permissions, payments and anything that touches personal data.
Security scanning. Dependency checks, secret scanning and static analysis in the pipeline, not as a one-off before launch.
A named owner. An engineer who understands every part of the codebase and is accountable for it, not a codebase nobody fully understands.

If your product itself uses an AI model, for example a chatbot over your documents or an agent that takes actions, there is a second set of risks. The OWASP Top 10 for LLM Applications 2025 lists them, including prompt injection, sensitive information disclosure, excessive agency and unbounded consumption (OWASP). The UK NCSC’s guidelines for secure AI system development cover secure design, development, deployment, and operation and maintenance (NCSC). Both accessed 18 October 2026. Our secure AI development service is built around those risks.

Is AI-first or “vibe coding” becoming normal inside software agencies?

Using AI tools is normal; vibe coding, in the sense of accepting generated code without really reading it, is not, at least among professional developers. In the Stack Overflow 2025 survey, 72.2% of respondents answered “no” when asked whether vibe coding is part of their professional development work, and a further 5.3% answered “no, emphatically” (Stack Overflow, accessed 18 October 2026).

For you as a client, this means two things. You should expect an agency to use AI tools, and you should expect it to explain how it controls them. The second matters more than the first. Ask these questions, ideally in writing, alongside the wider vetting questions in our guide on how to choose a software development company.

Review and quality

Which AI tools does your team use, and for what kind of work?
Does a person review every AI-assisted change before it is merged? Who, and how?
What automated tests and security scans run on every change?

Your code and data

Will our code, documents or data be sent to an AI provider? Which one, and under what terms?
Are those tools configured so our code and prompts are not used to train the provider's models?
Will any real personal data be pasted into an AI tool, or do you use test data only?

Ownership and licensing

Does the contract assign us the rights in everything you deliver, including AI-assisted code?
How do you check that generated code does not reproduce third-party code under an incompatible licence?
If something AI-generated causes a defect, is it covered by the warranty like any other code?

On ownership, the law is still developing and differs by country. In the US, the Copyright Office concluded in January 2025 that AI output can be protected only where a human author has determined sufficient expressive elements, and that the mere provision of prompts is not enough (U.S. Copyright Office, accessed 18 October 2026). Whatever the position where you are, a written assignment of everything delivered, plus a statement from the supplier about how it handles generated code, protects you better than silence. This is general information, not legal advice.

What should you still pay a developer for?

Pay for judgement and accountability, not keystrokes. Typing code was never the expensive part of a good project, and it is now the cheapest part. These are the things worth paying for.

Deciding what to build
Turning your idea into a small, testable first version, and saying no to features that do not prove anything. The cheapest code is the code you never needed.
Architecture and data model
Choices that are expensive to reverse later: how data is structured, how users and permissions work, what the product depends on and what it will cost to run.
Integrations that work when things go wrong
Payment providers, CRMs and accounting systems fail, time out and change. Handling that properly is where a lot of real engineering time goes.
Security and data protection
Access control, secrets, dependency risk and, if your product uses an AI model, the risks specific to that. A person needs to own this.
Testing, release and operations
Automated tests, a safe deployment pipeline, monitoring, backups and someone who responds when production breaks.
Accountability
A contract, a warranty and named people who will fix what they delivered. No AI tool signs those.

If you are still settling scope, the SaaS MVP planning checklist and the software requirements document template help you write it down before anyone writes code, by hand or with AI.

Where AI Dev Planner fits, and when we are not the right choice

We use AI coding tools in our own work. Every change is reviewed by an engineer and goes through automated tests before it is merged. We do not claim AI makes our work better on its own; the review and testing are what make it safe to use. Ask us the questions above and we will answer them in writing.

We fit when you need a product built with AI tools under engineering control, through our AI development services; when your product includes AI features that need to be secure, through secure AI development; or when you have an app built with Lovable, Bolt, Cursor or similar that needs to be made ready for real users, through vibe-coded app rescue.

We are not the right choice if:

you need a brochure site or landing page; an AI website builder is likely to be better value;
you want a prototype to test an idea quickly; build it yourself with an AI tool first;
you are looking for the lowest possible price by skipping review and testing; we will not deliver code that way;
you need a supplier on site, or your contracts require your data to be accessed only from your own country; our engineering team works remotely from Islamabad, Pakistan.

FAQs about AI and the cost of software

Can I build my app myself with Lovable, Bolt or Cursor and skip hiring a developer?

For a prototype, a demo or an internal tool used by a few trusted people, often yes, and it is a cheap way to test an idea. Before real customers, payments or personal data go through it, have someone experienced review authentication, access rules, data handling and deployment. That review costs far less than a rebuild, and far less than a breach.

Is AI-generated code secure enough for production?

Not by default. Veracode tested more than 100 large language models on 80 coding tasks and reported that the generated code introduced security vulnerabilities in 45% of cases. AI-written code can reach production safely, but only through the same controls as human-written code: review, automated tests, dependency and secret scanning, and a person accountable for the release.

Who owns code that an AI tool wrote for my project?

It depends on the country, and the law is still developing. In the US, the Copyright Office concluded in January 2025 that AI output is protected only where a human author has determined sufficient expressive elements, and that prompts alone are not enough. Whatever the law, your contract should assign to you the rights in everything delivered, and the supplier should confirm that generated code does not copy third-party code under incompatible licences. Take legal advice on the wording.

Should I ask an agency for a discount because it uses AI?

Ask what the saving is and where it goes, rather than for a blanket discount. Faster boilerplate can mean a lower price, more features for the same price, or more time on testing and security. Any of those can be fair if it is explicit. A lower price bought by skipping review or testing is not a saving.

How do I know if my vibe-coded app is ready for real users?

Check the basics: can one user see another user's data, are secrets kept out of the code, do payments and sign-up handle failures, is there a backup, and can you deploy a fix without breaking something else? Our free app readiness checker walks through these points for apps built with AI tools.

Will AI make software maintenance cheaper?

Some maintenance tasks, such as upgrading dependencies or writing missing tests, can go faster with AI help. But the 2025 DORA report found that AI adoption still has a negative relationship with software delivery stability, so more code shipped faster can mean more to maintain. Budget for maintenance as you would without AI, and judge any saving on evidence.

Is it a red flag if a development agency does not use AI at all?

Not on its own. Good software was built before these tools and still is. What matters is whether the team delivers reviewed, tested, documented code on time. If an agency does use AI, the more useful question is how it controls the output, which is what the questions in this guide are for.

Sources

Each source is listed with what we used it for and the date we accessed it.

Stack Overflow Developer Survey 2025: AI section — 84% using or planning to use AI tools; 51% of professional developers using them daily; 46% distrust and 33% trust AI accuracy; 66% frustrated by answers that are almost right; 45.2% find debugging AI code more time-consuming; vibe coding answers. Accessed 18 October 2026.
Google Cloud: Announcing the 2025 DORA report — Nearly 5,000 respondents; 90% use AI at work; more than 80% believe it increased productivity; 30% little or no trust in AI-generated code; positive relationship with throughput, negative relationship with delivery stability. Accessed 18 October 2026.
DORA: State of AI-assisted Software Development 2025 — AI as an amplifier of an organisation's existing strengths and weaknesses. Accessed 18 October 2026.
METR: Measuring the impact of early-2025 AI on experienced open-source developer productivity — 16 experienced developers, 246 real issues; tasks took 19% longer with AI; developers expected a 24% speed-up and afterwards believed they had been sped up by 20%. Accessed 18 October 2026.
METR: We are changing our developer productivity experiment design (February 2026) — METR believes developers are likely more sped up in early 2026 than in early 2025, but calls its new data very weak evidence for the size of the change. Accessed 18 October 2026.
Peng et al., The Impact of AI on Developer Productivity: Evidence from GitHub Copilot (arXiv) — Controlled experiment: the group with Copilot completed a JavaScript HTTP server task 55.8% faster; the study does not examine code quality. Accessed 18 October 2026.
Veracode: 2025 GenAI Code Security Report press release — Across 80 coding tasks and more than 100 large language models, AI-generated code introduced security vulnerabilities in 45% of cases. Accessed 18 October 2026.
OWASP Top 10 for LLM Applications 2025 — The ten most significant risk categories for applications built on large language models, from prompt injection to unbounded consumption. Accessed 18 October 2026.
NCSC: Guidelines for secure AI system development (UK) — Secure design, development, deployment, and operation and maintenance of AI systems. Accessed 18 October 2026.
U.S. Copyright Office: Copyright and Artificial Intelligence, Part 2: Copyrightability (US) — AI outputs are protected only where a human author has determined sufficient expressive elements; prompts alone are not enough. Accessed 18 October 2026.

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