The AI-builder pitch goes: "build a SaaS in a weekend instead of three months". The custom-development pitch goes: "AI tools are toys; serious products are built by engineers". Both pitches are wrong, both for the same reason — they collapse a multi-axis decision into a single price tag.
The honest comparison is across at least seven cost lines, over a 12-month window, with both approaches priced fairly. This is that comparison, with numbers from actual European tech salaries, real Stripe and AWS pricing, and the assumption that you ship something a real customer will pay for in week one.
The product we're costing
To make the comparison concrete: a single-tenant SaaS product. Marketing site, signup, password auth, three-tier Stripe billing, a per-customer dashboard with five views, an admin console for the founder, transactional email, daily backups, EU hosting, basic accessibility, and a small public docs site. About what every B2B SaaS in 2026 needs to charge €30/month.
We assume launch in month one and growth from zero to 200 paying customers across the year. The total revenue at year-end is in the €60,000–€80,000 range. The product survives — it's not a hit, but it's not a failure either.
Custom development, fully loaded
Two engineers (one full-stack mid, one back-end senior, both Estonia-priced) at €5,500/month gross each, with social tax pushing each total to about €7,200/month. Total engineering cost: €172,800 over 12 months.
You also need:
- Design: a part-time contractor at €1,500/month for the first six months, then quarterly. ~€12,000.
- Hosting: AWS or Hetzner-class infra, ~€200–€300/month for a real production stack. ~€3,000.
- Third-party services: Stripe (free until you process), Resend or Postmark for email (€60/month), Sentry (€26/month), Datadog or Honeycomb (€100/month for the small tier), GitHub Actions, Linear, password manager, password resets, etc. ~€3,500.
- Domain, SSL, monitoring, miscellaneous. ~€500.
- Recruiting cost: if either engineer is a new hire, expect 1–2 months of salary equivalent in recruiter fees, time, and productivity ramp. ~€10,000–€15,000.
Total all-in for 12 months: ~€207,000. Add another €15,000 if you want a junior PM or someone to talk to customers full-time. Round number: €220K.
What you actually get for €220K
The real question isn't whether €220K builds a SaaS. It does. The real question is what fraction of those 12 months gets spent on the things that distinguish your product, versus the things every SaaS has — auth flows, billing webhook reconciliation, admin consoles, error pages, password reset emails.
The honest answer, by week-on-week observation across many startups: about 40% of the year goes to your unique product, and 60% goes to the SaaS plumbing every other product also has. €130K of that €220K is engineering nothing differentiated.
AI builder approach, fully loaded
Marcus Builder tier, the project as one billable unit: €29/project/month. €348/year.
That covers: hosting, SSL, custom domain, the full SaaS scaffolding (auth, billing, admin, multi-tenant data, transactional email, daily backups, EU region), edits unlimited via natural-language instructions, and a clean static + Git export at any point if you want to take the code elsewhere.
You still pay for:
- Domain: €10/year.
- Stripe: free until you process; standard 2.9%+€0.30 fees on the €60–€80K of revenue. ~€2,400.
- Email volumes above the included tier, if you grow into them. ~€500.
- The founder's salary, which we're holding constant for fairness. €60K–€80K depending on cost of living. Use €70K as the midpoint.
- One contractor month for design polish at month four, when the design is "good enough" but you want it to look like an in-house team did it. ~€2,500.
Total all-in for 12 months: ~€75,800. Round number: €76K.
Difference vs custom: about €144K saved in the first year, the difference between hiring two engineers and not.
Now the honest part: where Marcus loses
The €76K vs €220K gap looks like an open-and-shut case for AI builders. It isn't. It depends entirely on whether your product is in the 80% of SaaS that's mostly plumbing, or the 20% that has serious technical differentiation.
Marcus, and AI builders broadly, lose on these axes in 2026:
- Custom data structures the model can't predict. If your product depends on a graph database, a custom search index, a real-time pipeline, or any structure outside the patterns AI builders trained on, the AI version will get you 70% there and the last 30% becomes a fight. Hire engineers.
- Latency or throughput targets. Marcus ships responsive, but it ships generic infra. If you need to serve 50ms p99 globally, or process a million events a minute, or run on edge workers, you need engineers who care about the bottom line of latency, not generic-good-enough.
- Anything that needs deep integration with a specific industry's quirks. Banking, healthcare, regulated trading, legacy ERP integrations — the model knows about them but doesn't know your customer's exact bespoke version. Engineers who've worked in the industry win.
- Long-lived products with deep technical debt. A product you'll maintain for ten years deserves engineers who own its design and can refactor as the world changes. AI builders are right for the first 18 months. Past that, the right move is often to take the static + Git export and bring engineers in to own the codebase.
The cases where Marcus wins are also clear:
- The first 12 months of any new product, regardless of where it ends up.
- Internal tools, ops dashboards, audit consoles. The kind of software that's "important to your team and uninteresting to engineers".
- Marketing sites and landing pages that need real form handling, real billing, real analytics — i.e. landing pages that are 70% "real product".
- Personalised demos for sales, where each demo is bespoke and engineering has no business doing it.
- Per-customer micro-sites, embedded portals, white-label thin clients — the things where the long tail of "almost the same but not quite" is the actual product.
The opportunity cost line
Here's the line nobody puts in the spreadsheet: the cost of waiting twelve weeks instead of one.
If your idea is right, every week you're not in market is a week of compounding learning lost. Founders who ship in week two get to interview customers, pivot the offer, and price-test in week three. Founders who ship in week twelve start that loop in week thirteen. By week twenty, the early-shipping founder has run twelve customer iterations to the late-shipping founder's three.
That's not a money cost on the spreadsheet, but it's the most expensive line on the project. The €144K of cash savings that AI builders deliver is dwarfed by the value of being in market eleven weeks earlier — if your idea was right.
And if your idea was wrong, you've spent €76K to find out instead of €220K. Either way the math is decisive.
Where the line is
The honest decision rule, after watching this play out across many founder cohorts:
- If your product is mostly SaaS plumbing dressed in your particular use case — start with an AI builder. The fastest way to know whether your idea is good is to put it in front of paying users.
- If you cross €30K MRR and start needing custom infrastructure, hire your first engineer and own the codebase. Marcus exports clean — the transition is a real working starting point, not a translation problem.
- If your product has deep technical differentiation from day one — real ML, real data infra, real latency or scale concerns — start with engineers and skip the AI-builder phase. You'd outgrow it in week eight anyway.
That's it. Most founders are in case 1, convince themselves they're in case 3, and burn €144K finding out they were wrong.