Custom-built accounting software vs SaaS: The true price of vibe coding
The temptation has never been stronger.
You open ChatGPT, Claude, or Cursor, describe the close management workflow you’ve been dreaming about, and within an hour you’re looking at something that actually works. It pulls data. It calculates. It even looks professional. Your CEO sees the demo and says, “Why are we paying six figures a year for software when we can just build this ourselves?”
We get the appeal. Between the two of us, we have spent our careers at the intersection of accounting and software, and we have seen this question from every angle.
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So when we say we understand the pull toward building your own custom accounting software now that AI makes it feel so accessible, we mean it. We have both vibe coded apps to help our work at Netgain and see real benefit. These experiences are exactly why we want to have an honest conversation about what AI in accounting software actually changes in the build-vs-buy equation, and what it doesn’t.
AI changed the “build.” It did not change the “maintain.”
There’s no question that AI has lowered the barrier to creating software. The term “vibe coding” (sometimes called “vibe accounting” when applied to finance tools), using generative AI to translate natural language prompts directly into functional code, has gone from a novelty to a boardroom talking point. When Anthropic launched Claude Code in early 2026, roughly $285 billion in market value evaporated across software, financial services, and asset management companies in a single trading session. The market’s message was clear: if anyone can build software now, what is a software company worth?
But here’s what the market panic missed, and what we have learned from building and shipping accounting software used by real finance teams every day: creating software was never the hard part. Maintaining it is.
AI may have made this problem worse.
The code quality problem is real, and it’s measurable
We both use AI daily, and we believe it is transforming how we work. But we also come from accounting backgrounds, which means we follow the numbers. And the numbers on AI-generated code quality should give every finance leader pause.
CodeRabbit’s State of AI vs. Human Code Generation Report, analyzing 470 open-source GitHub pull requests, found that AI-generated code contains 1.7 times more issues per pull request than human-written code: 10.83 issues versus 6.45. And a Cortex Engineering in the Age of AI benchmark report found that while pull requests per developer increased 20 percent with AI assistance, incidents per pull request jumped 23.5 percent, and change failure rates rose around 30 percent.
Here’s the number that should keep every CFO up at night: Gartner predicts that over 40 percent of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, and inadequate risk controls. If enterprise AI projects broadly face that rate of failure, imagine the odds for a one-off accounting tool built over a weekend.
That’s a time bomb with a two-year fuse.
When AI writes your accounting logic, who is accountable?
This is where our accounting backgrounds make us particularly nervous.
Morgan spent years working with the Chief Accounting Officers of Fortune 100 companies. The number one thing those leaders cared about was controls: who owns the process, who validates the output, and who is on the hook when something is wrong. A quick vibe-coded tool does not answer any of those questions.
When a purpose-built accounting software provider develops your intercompany elimination logic, your depreciation schedules, or your ASC 842 compliance workflows, there is an organization standing behind that code. They have CPAs on staff. They have QA processes. They maintain SOC 2 Type II certifications. They update the product when regulators issue new guidance.
When your team vibe codes a consolidation tool, who owns the output? As Fortune reported in April 2026, when AI tools begin to generate production-ready code automatically, the bottleneck is shifting from writing software to verifying it. And in accounting, verification is not optional.
AI coding assistants also routinely suggest packages that don’t exist. Researchers at the University of Texas at San Antonio analyzed 576,000 code samples across 16 popular large language models and found that 21.7 percent of package suggestions from open-source AI models referenced nonexistent dependencies, which attackers can weaponize through a technique known as “slopsquatting.” Even commercial models hallucinated packages 5.2 percent of the time. For software handling financial data, this is an audit finding waiting to happen.
The compliance and security gap just got wider
Becca worked with clients ranging from large corporations to VC partnerships at Deloitte, and one thing was consistent across every engagement: the cost of getting compliance wrong always exceeds the cost of getting it right the first time.
IBM found that breaches in financial services cost an average of $6.08 million, 22 percent above the global average of $4.88 million.
Now layer on the AI governance dimension. The U.S. Department of the Treasury, in coordination with the Cyber Risk Institute and more than 100 financial institutions, released the Financial Services AI Risk Management Framework in February 2026. It introduces 230 control objectives spanning governance, data, model development, validation, monitoring, third-party risk, and consumer protection, all structured around the NIST AI RMF. Meanwhile, Gartner projects AI governance platform spending will reach $492 million in 2026 and surpass $1 billion by 2030, as regulatory requirements intensify globally.
When you buy from a SaaS provider, you are buying their compliance infrastructure too. When you build with AI, every one of those 230 control objectives is your problem to solve.
The person who built it is the only one who understands it (and that is being generous)
In the old world, the risk was that the one developer who built your custom SuiteScript solution would leave and take all the institutional knowledge with them. In our work at Netgain we have seen this play out with client after client.
AI-generated code makes this problem worse. The person who prompted the AI to generate your custom tool may not fully understand the code that was produced. We are finding that as time goes on people are evaluating the code their LLMs produce less not more. But at the same time, Stack Overflow’s 2025 Developer Survey found that trust in AI code accuracy dropped from 40 to 29 percent year over year. More developers now actively distrust the accuracy of AI tools (46 percent) than trust it (33 percent), and only 3 percent report “highly trusting” the output. This means that actual developers don’t trust vibe coding, while accountants who don’t understand code are trying to vibe code.
In addition to whether it works properly or not, if the person who generated the code doesn’t fully understand it, what happens when they leave? You are not only losing institutional knowledge; you are also inheriting code that nobody fully comprehends.
So when does it make sense to build?
We are not anti-DIY. We love to vibe code our own internal tools, and some of it genuinely works. We built a Slack bot for our team to ask technical product questions about our products and it has been a real win. Internal assistants like that, along with dashboards, one-off data transformations, and organization-specific workflow automations, are legitimate use cases: low-stakes, and survivable if they break.
Even there, the pattern in this article shows up. An internal product-usage dashboard and an agent that pulls from our sales resources to summarize deal status and cycles both worked — right up until each needed an actual developer to come in and clean up the code. And now we are doing the very thing we caution against: trying to figure out who owns them. If ownership is an open question for a usage dashboard, it is a far bigger one for the software your auditors will ask about.
The calculus of when to build and when to buy finance software has not changed. AI has just made the initial build look cheaper while leaving the ongoing costs, risks, and accountability gaps exactly where they’ve always been. Or worse.
The question we’d encourage every CFO and Controller to ask is not “Can AI build this for us?” It is: “Who maintains it at 2 AM during quarter-end close when something breaks? Who updates it when the next ASC standard drops? Who is accountable when the auditors ask how it works?”
If the answer to those questions is an accounting software partner whose entire business is built around solving these problems, a team of CPAs, accountants, and engineers who live and breathe this work, the decision to buy finance software rather than build it starts to look less like an expense and more like an advantage.
