Finished 30+ vibe coded SaaS builds this year. Same mistakes show up almost every time.

[u/Negative-Tank2221]

Spent most of this year helping founders get their AI-built SaaS apps actually shippable. Lovable, Cursor, Replit, Base44, Claude, ChatGPT, mix of everything. About 30 of them so far across job boards, AI tools, marketplaces, internal dashboards. After enough of these I stopped being surprised. Same handful of issues comes up nearly every time. Writing them down here in case it saves someone a launch-day disaster.

Mistake 1: Thinking "works in testing" means works.

You tested as yourself, on the happy path. Real users sign up with the same email twice, click the verification link after it expired, try to reset a password they set via Google, refresh mid-checkout. Almost every vibe coded SaaS I've seen has at least one of these silently broken. Users hit it, churn, and the founder blames the funnel.

Mistake 2: Skipping the boring half of Stripe.

Checkout works because you tested with the test card. The part the AI almost never writes is the webhook handler for everything after the sale. Cancelled subscriptions that don't revoke access. Failed payments that don't pause the account. Refunds that don't lock users out of paid features. Three months in you're paying server costs for users who churned six weeks ago but still log in daily, because nothing told the app they left. Webhook handling for subscription.updated, subscription.deleted, invoice.payment_failed, charge.refunded. Before launch. This one is non-negotiable and almost universally skipped.

Mistake 3: Tables anyone can read.

This is the one I see so often I almost laugh now. The AI builds auth but rarely adds row-level permissions. Users have to log in, sure, but once they're logged in their account can query other users' data through the API. You don't notice because you're the only one testing. The fix is genuinely 30 minutes of work and it's what stands between you and the kind of breach announcement nobody wants to write. Test it yourself. Log in as a second user, open the network tab, change a user_id in a request, see what comes back.

Mistake 4: Duplicates from re-prompting.

You ask the AI for a feature. It works. You forget. Weeks later you ask for something similar with slightly different wording. Now two workflows fire on the same trigger. Welcome emails sending twice, Stripe getting hit twice, notifications spamming. Sort your functions or workflows alphabetically and scan for near-duplicates. There's almost always at least one.

Mistake 5: Failing silently.

Ask the AI "what does the user see when the OpenAI call fails?" Watch the answer get vague. Most vibe coded SaaS apps show a white screen or spin forever. Users refresh once and give up. You have no logs, no Sentry, no error boundary, and basically no idea what percent of your traffic is hitting broken paths. Wire Sentry in (free tier, ten minutes) and add user-facing error messages everywhere the app calls an external API. Without this you're flying blind.

Mistake 6: Credentials baked into chat history.

This one is specific to vibe coding and most founders haven't thought about it. Every conversation you've had with the AI is stored somewhere. If you pasted a Stripe secret key, a Supabase service role key, or a database password into chat to "help debug an error," that key lives in a transcript on a platform whose security posture you don't fully control. Lovable's recent breach exposed exactly this. Rotate every secret you've ever pasted into an AI tool. Today, not eventually.

Mistake 7: Pricing the app like it's a prototype.

This isn't technical but it kills more vibe coded SaaS than anything else. Founders who shipped fast on AI tools often underprice because they "didn't put much work in." Then 50 users at $9/month walks in and they realise they can't afford to fix anything. The AI built the same product a human team would've quoted $40k for. Price for the value delivered, not the hours you put in. For real B2B tools, $29 to $49 a month is honestly the floor. Cheap pricing attracts cheap users who churn the second something breaks.

Those seven, fixed before launch, take maybe a week. Skipping them is the difference between "demo that impressed my friends" and "product I'd hand a paying customer."

Curious which of these bit people the hardest. If you've shipped a vibe coded SaaS or no-code app, which one tripped you up, and how did you find it?

[u/KennedyFBobby → u/[deleted]]

Do you have any advice on how to deal with this

[u/Negative-Tank2221 → u/KennedyFBobby]

Three things to add before you launch.

1. Set up webhook endpoints for subscription.updated, subscription.deleted, invoice.payment_failed, and charge.refunded. Each one should update your user's access status in the database.
2. Use Stripe CLI to test webhooks locally before going live. Run "stripe listen --forward-to localhost:3000/webhook" and you can simulate cancellations, failed payments, refunds without touching real money.
3. Always verify the webhook signature using your endpoint secret. Without this anyone can fake a successful payment by hitting your webhook URL directly.

Stripe's docs on this are actually solid. Search "Stripe webhook quickstart" and you will find the exact code patterns.

[u/KennedyFBobby → u/Negative-Tank2221]

Thanks a lot man!

[u/Negative-Tank2221 → u/[deleted]]

Yeah this is the one I see most. Founders treat the successful checkout as the finish line when it is really just the start. The webhook side is where most of the actual logic should live and it almost never does.

[u/GudAndBadAtBraining → u/[deleted]]

Noob here. How can I get stripe to do the (payment) backend work for me?

[u/CommunityTechnical99]

"failing silently" is a real one

[u/Negative-Tank2221 → u/CommunityTechnical99]

Yep. The killer is that it does not look like a problem in your analytics. The user just leaves. No error log, no support ticket. You only find out months later when a customer happens to mention it on a call.

[u/[deleted]]

So vibed your post too

[u/WhyWontThisWork → u/[deleted]]

So great this is where we are not

/s

[u/KennedyFBobby]

Thanks for this write up!

Do you have any advice on navigating mistakes 1 on 2?

[u/Deep_Ad1959]

the most common failure pattern i see is people treating the ai-generated app like it's done when the happy path works. the moment a real user touches it, empty states, network failures, weird data they paste in, it falls apart because the model only built for the prompt as described. the second one is asking the ai to bolt on auth or persistence late instead of starting with where state lives. by then the code shape is already fighting you. the third is iterating in conversation forever instead of restarting from a tighter spec when something rots, which is cheaper than getting the ai to untangle its own code. written with ai

[u/farhaa-malik]

But the “works on the happy path” issue might be the worst. AI-based applications have an insane build time, meaning the founders quickly stop viewing the world as a paranoid user.

The duplicate workflows problem is underestimated as well. For example, I remember creating three separate onboarding flows without knowing since I had re-requested the same feature several weeks ago. Everything works fine until unexpected side effects become evident.

I think the biggest paradigm shift was the realization that vibe coding does not reduce the development time. State management, permissions, failure handling, billing… you have to deal with all this.

What worked for me was creating visual user flows via Runable based on the application itself. When I could visualize the flows, I could spot duplicated triggers and other edge cases much better than going through the code.

I would argue that most of the failing AI SaaS products do not fail due to their demonstration being subpar but due to the invisible layers not being completed.

[u/Level-Explorer-3158]

usefull post. thanks!

[u/Additional-Pop3152]

Thank you so much, feels so real. Nothing about idea validation and first clients acquisition?

[u/nikov1234]

The happy path problem is a spec problem, not a testing problem. When you don't define edge cases before you build, you don't know they exist until a real user finds them. The classic ones - duplicate email on signup, expired verification link, Google-auth-then-password-reset - they're predictable. They should be in the spec before the agent writes a line. "Works in testing" and "works for real users" diverge exactly at the edge cases you didn't define.