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SaaS platform, custom store or vibe coding: what to choose

| Article: 7 min read | Launch and platforms

A SaaS platform is a ready-made online store service on subscription: the provider runs servers, updates and security, and you set up the catalog and design. A custom store is a solution written for your processes, with code that belongs to you. Vibe coding is building an app by describing the task to an artificial intelligence, when the code is written almost without manual engineering work (vibe coding).

There is no single right answer: the best option depends on the stage of the business. This article does not recommend any platform and does not rank one approach above another.

Honestly about the start: to test an idea, the simplest thing is enough

If you don't yet know whether people will buy your product, any of the three options works. A ready-made platform gives you a storefront in days, and an app assembled through AI gives a prototype of an order page even faster. The goal of this stage is not beautiful code but an answer to the question "do people pay". Technical questions are secondary here: you can throw everything away and start from scratch if the hypothesis isn't confirmed.

What is worth doing at this stage regardless of the choice:

  • Don't keep buyers and orders in a place from which they can't be exported.
  • Connect payment and delivery through proven services (payments in Ukraine), not home-made solutions.
  • Don't enter buyers' personal data into tools whose terms you haven't read (GDPR).

The three options in essence

1. SaaS platform

Strengths (this is practical logic, not a research result: there is no fresh independent comparison for 2025–2026):

  • Fast launch. The core functions (catalog, cart, checkout, standard integrations) are already there.
  • Low upfront investment. You pay a subscription, not development. Costs are spread over time.
  • Fewer worries. Hosting, security, updates and backups are on the provider's side.
  • Less risk of "depending on a programmer". A person without development skills can set up the store.

Limitations:

  • You work within what the platform allows: non-standard processes have to be fitted into its logic or worked around with add-ons.
  • The subscription and fees grow with your volumes; at scale they can become a substantial cost line.
  • Moving to another platform requires work with data and URLs (migration).
  • The speed and behavior of the site partly depend on the provider.

2. Custom store

Strengths (also practical logic):

  • Control. You decide how the catalog, prices, checkout and delivery work, and you own the code and data.
  • Complex processes. Non-standard B2B price lists, configurators, discount rules, complex stock across several warehouses don't have to be bent to someone else's model.
  • Integrations. Deep exchange with the accounting system (ERP), CRM, warehouse and delivery without platform limits.
  • Speed. You can remove the unnecessary and optimize the site for your own scenario, not for a universal set of features.
  • Cost at scale. When volumes are large, the fixed cost of a team may turn out lower than the variable payments to a ready-made service. This depends on your numbers, so calculate rather than trust general rules.

Limitations: a longer and more expensive start, a need for a team or contractor for support, responsibility for security and updates. Unattended code ages quickly.

3. Vibe coding

AI tools have genuinely made building apps easier. But a few months out, the question changes from "does it work" to "who maintains it". What recent sources say:

  • Security. Veracode tested more than 100 language models on 80 tasks for code with weaknesses under the MITRE CWE classification and found that when choosing between a secure and an insecure way, the models chose the insecure one in 45% of cases; the security level did not improve as new models came out, although syntactically the code got better (report of 30.07.2025) (Veracode). This is a test on pre-selected tasks; it does not mean that 45% of any AI-written code has vulnerabilities.
  • Developer trust. In the 2025 Stack Overflow survey, 46% of respondents do not trust the accuracy of AI tools and 33% do; 66% name solutions that are "almost right, but not quite" as a frustration (Stack Overflow Developer Survey 2025).
  • Speed of work. A METR randomized study (16 experienced developers, 246 tasks, early 2025) showed that tasks took 19% longer with AI tools, although participants believed they worked faster (METR, 10.07.2025). In February 2026 METR reported that data on later tools showed a nominal speedup (roughly 18% and 4% in two groups of developers) with confidence intervals that include zero, and because of participant selection problems it considers them unreliable, so the study design is being changed; the authors believe developers in 2026 are probably faster than in 2025 but cannot yet measure it reliably (METR, 24.02.2026). Conclusion: this research can neither prove nor refute the speed benefit of AI.
  • Stability. The DORA 2025 report (about 5,000 respondents): AI amplifies what is already in the team; since 2025 AI use is positively associated with delivery throughput but still negatively with its stability, unless there is strong automated testing and fast feedback (Google Cloud, 24.09.2025).

Technical debt when code is written without engineers

Technical debt is the accumulated price of quick decisions in code: what already works but makes every next change harder. For code without engineering oversight it appears quickly, for these reasons:

  • nobody is responsible for the architecture: the author can't explain why it was written that way, because AI wrote it;
  • there are no tests: any edit can break checkout or payment;
  • security and library updates have no owner;
  • code duplication. A GitClear analysis for 2020–2024 (211 million changed lines) showed that blocks of five or more identical lines grew eightfold in 2024, and moved lines, a refactoring indicator, fell by 39.9% (DevClass, 20.02.2025). GitClear sells code review tools, the data comes from its customers and open projects, and duplication by itself does not prove more bugs.

This is not a rejection of AI. DORA itself shows that AI amplifies practices already in place: where there are tests, code review and someone responsible for the system, the benefit is greater.

Comparison by stage

Stage

What matters

What to choose

Why

Idea, no sales

Speed, minimal costs

Simple SaaS or a prototype (including one built through AI)

The goal is to test a hypothesis; the code can be thrown away

First sales (dozens of orders a month)

Reliability of payment and order handling

SaaS or a proven prototype with manual processing

Manual work is still cheaper than development

Steady sales, growth

Automation, margin, speed

SaaS with integrations; assess the limits

It is now visible what exactly gets in the way

Complex processes (B2B price lists, several warehouses, non-standard logic)

Control and integrations

Custom store or a hybrid (ready-made base plus your own modules)

The ready-made service starts to constrain

Large volumes, high subscription cost

Cost of ownership

Calculate: subscription and fees versus a team

The decision depends on your numbers

An AI prototype grew into the main store

Security and support

Engineering audit, tests, a responsible person; then decide whether to rewrite or improve

Otherwise technical debt accumulates

The table is a practical guideline, not a research result.

When to move from one to the other

Signs that SaaS no longer fits:

  1. You constantly work around platform limits: manual work, Excel exports, patches between systems.
  2. A specific process that brings money (special price or delivery logic) can't be implemented.
  3. The subscription and fees at your volume become a significant part of margin: calculate in unit economics, not by eye.
  4. Site speed or catalog limits slow down sales.

Signs that an AI prototype should be put under engineers' oversight:

  1. Real money and buyers' personal data pass through it.
  2. Any change is scary because it's unknown what will break.
  3. There have already been payment failures, lost orders or security incidents.
  4. The prototype's author can't explain how it works.

Before moving: draw up a technical specification, a list of data to export and a plan for moving URLs. An intermediate option is headless architecture: a ready-made backend, your own storefront.

Questions to ask yourself

  1. Is there a process in my sales that a ready-made service doesn't cover?
  2. Who will maintain the solution in a year: a team, a contractor or nobody?
  3. How much will I pay over 3 years: subscription, fees and customizations versus development and support?
  4. How will I export data and URLs if I want to leave?
  5. Can the task be solved by configuration before developing anything?

Common mistakes

  • Comparing only the initial price and not counting the subscription with fees and support over several years.
  • Building a custom store "just in case" when the needs are not yet clear.
  • Leaving an AI prototype as the main store without a security check and tests.
  • Not finding out in advance how to export data.
  • Treating comparisons from platform vendors or agencies as neutral (including this one: the article has no commercial interest, but check the numbers against your own books).

FAQ

What is better for an online store: SaaS or custom development? It depends on the stage. For testing an idea and first sales, a ready-made platform is usually enough. A custom store is justified when processes are complex, there are many integrations, or the subscription at large volumes costs more than a team.

Can an online store be built through AI (vibe coding)? A prototype, yes. For a store with real payments and buyers' data you need tests, a security check and a responsible person, otherwise technical debt accumulates.

What is technical debt? The accumulated price of quick decisions in code: it works, but every new change is more expensive and riskier. More in technical debt.

When should I move from SaaS to a custom store? When the platform's limits cost you more than development and support, and this is visible in numbers, not just in feelings.

Can the two approaches be combined? Yes: a ready-made base plus your own modules for what it doesn't cover, or headless commerce.

Sources

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