When Waterglass acquired Notehouse in 2025, it did not buy a beautiful piece of software.
Notehouse was a six-year-old CRM for nonprofit leaders, social workers and counsellors. It had about $100,000 in annual recurring revenue, 250-plus customer organisations and a 4.7-star Capterra rating. It also had years of technical debt. Waterglass founder Bernhard Hauser later said engineering diligence pointed toward one conclusion: much of the codebase should be replaced.
The buyer went ahead anyway, then rebuilt the application.
That sounds irrational only if the code was the asset. Hauser’s explanation was different: Waterglass had acquired an audience, recurring revenue and a place inside a specialised workflow. The purchase price was never disclosed. But that almost misses the point. Waterglass wasn’t really buying the code. It was buying customers, recurring revenue and a place inside a specialised workflow.
That distinction matters now because AI is changing the economics of software faster than it is changing the economics of distribution.
Code Is Getting Cheaper. Customers Are Not.
The barrier to producing software has fallen sharply. A 2025 field study involving 4,867 developers at Microsoft, Accenture and a Fortune 100 company found that developers given an AI coding assistant completed about 26% more tasks. Y Combinator managing partner Jared Friedman said that in the accelerator’s Winter 2025 batch, a quarter of startups had codebases that were 95% AI-generated.
Reliable software is not trivial—security, architecture, QA and maintenance still matter. But “we can build the product” is becoming less impressive as a standalone advantage.
Finding customers has not enjoyed the same deflation.
Benchmarkit’s 2025 SaaS benchmarks found that the median company spent $2 in sales and marketing to acquire $1 of new annual recurring revenue in 2024, up 14% from the previous year. The weakest quartile spent $2.82. It is not a universal formula, but it captures the problem: software supply is exploding while attention, trust and switching willingness remain scarce.
So the founder-economics question changes. Why spend a year building an AI product and another two trying to manufacture demand if a small, ignored SaaS company has already done the second job?
The Ugly Software May Contain the Real Moat
Vertical SaaS is often unimpressive from the outside. The interface can look dated. The feature set may be narrow. Growth can be slow. Yet a scheduling tool for clinics, CRM for photographers or case-management platform for social workers may sit inside a workflow customers repeat every day.
That produces assets a fresh AI app lacks: customer relationships, billing history, usage patterns, support conversations, domain vocabulary, integrations and knowledge about what users actually do.
What we bought was access to an audience. — Bernhard Hauser
The small-acquisition market shows buyers will pay for those economics. Acquire.com’s 2025 transaction data, covering more than 136 deals with reported sale prices and multiples, showed profitable SaaS businesses selling at a median 3.9 times annual profit. Businesses with less than $100,000 in annual net income averaged 3.7 times profit. In rough terms, a $100,000 purchase at that multiple would correspond to about $27,000 of annual profit before deal-specific adjustments. Acquire.com’s sample naturally skews toward smaller, founder-led transactions, but it offers a useful view of a market that is usually opaque.
There are literal small examples too. Jonathon Ringeisen sold Essential Studio Manager, a niche CRM for photographers, for $90,000 through Acquire.com. A functioning product and customer base already existed.
Software Equity Group counted 2,698 SaaS transactions in 2025, up 28% year on year; 72% referenced AI in some form. Vertical software represented 55% of SaaS M&A activity in the first quarter of 2026. Buyers are increasingly interested in software that already owns workflows and data.
Buy the Demand, Then Add the AI
The contrarian play is not “buy old SaaS because old SaaS is better.” It is to separate customer acquisition from product improvement.
A founder starting from zero must discover a problem, ship a product, earn trust, find a channel, convert users, retain them and only then learn which AI features matter. An acquirer can begin halfway down that list.
AI then becomes an upgrade layer.
Support tickets can be classified automatically. Repetitive data entry can be reduced. Customer records can support better search, summaries or recommendations. Engineering and support costs can fall. Most importantly, those upgrades can be tested against paying users instead of an imagined market.
Waterglass followed almost exactly that sequence with Notehouse. It kept the customer base, rebuilt the application in roughly six months and used AI-assisted software engineering to accelerate the work. The crucial point is not that AI rescued bad code. It is that existing demand gave the rebuild somewhere to land.
That is the difference between buying existing demand and creating new demand. One uses capital to purchase a proven revenue stream and then improve it. The other uses capital to fund uncertainty.
I am much better at taking something that exists and bringing it to the next level. — Pascal Levy-Garboua
The Trap Is Buying a Problem and Calling It an AI Opportunity
This strategy can go wrong quickly.
Notehouse itself is a warning. During a payment migration after the acquisition, some customers had to reconfirm billing details and cancellations increased. Technical debt was so severe that the buyer chose a full rebuild. Because the product handled sensitive information, security and HIPAA compliance also mattered. A cheap acquisition can become expensive once migration, remediation and support work are counted.
Churn can turn $100,000 of ARR into $70,000 after twelve months. Customer concentration can turn “recurring revenue” into one renewal decision. Weak code may conceal unsupported libraries, security holes or a single developer who understands the system. Data may be legally restricted or useless for AI. Buyers can also overpay for growth customers never asked for.
The acquisition thesis has to work before the AI story does.
A Practical Acquisition Test
Before buying a small SaaS company, an operator should be able to answer seven questions:
- Is the revenue genuinely durable? Examine cohort retention, cancellations, failed payments, contract terms and gross revenue retention—not just MRR on the day of sale.
- How concentrated is the customer base? Calculate what happens if the largest one, three and five customers leave.
- How deeply does the product own a workflow? Daily or weekly operational use is more valuable than occasional convenience.
- What proprietary data or feedback loops actually exist? “We have data” is meaningless unless the buyer has lawful access and the data can improve the product.
- What will the code cost after closing? Commission independent technical, security and dependency diligence. Price the rebuild before signing.
- Can AI produce a measurable customer or margin improvement? Faster support, fewer manual steps, higher ARPU or lower operating cost is a thesis. “Add a chatbot” is not.
- Does the deal work without heroic growth? Compare purchase price with current owner earnings, required reinvestment and realistic churn. AI upside should be optionality, not the only way to justify the valuation.
That last point is where acquisition fantasies die. If the deal only works after doubling prices, halving churn and launching a perfect AI agent, the buyer is not acquiring certainty. The buyer is paying upfront for a turnaround.
FAQ
Is buying a small SaaS business safer than starting an AI company?
Not necessarily. It replaces product-market-fit risk with acquisition, technical and retention risk. The advantage is that revenue and customer behaviour can be diligenced before the purchase.
What makes a small vertical SaaS business attractive?
Strong retention, fragmented customers, recurring usage, healthy margins, low platform dependency and a workflow customers are reluctant to disrupt. A dated interface can be fixed. A customer base that never cared about the product cannot.
How much should a buyer pay?
There is no universal number. Acquire.com’s 2025 data put the median profitable SaaS transaction at 3.9 times annual profit and businesses below $100,000 in net income at 3.7 times on average. Those figures are a starting benchmark, not a valuation rule.
The most interesting AI company may therefore begin with software nobody wants to brag about owning.
The interface can be ugly. The code can need work. The category can sound boring at dinner.
But if customers keep paying, the workflow is embedded and the data is useful, the buyer starts with the one thing thousands of new AI apps are still desperately trying to manufacture: distribution.
In the AI era, the moat may not be who can build the smartest software. It may be who already has someone paying for it.