The Lovable SaaS that broke the moment real users arrived

A real project, kept anonymous at the client's request. A founder had built a CRM for Shopify store owners in Lovable and put it live as a paid product, with more than 50 people paying $10 a month to use it. It had passed the only test it ever got — the founder and a few friends who run stores, all using it gently, one at a time. Real users behaved differently, and nothing underneath was built to hold them: no rate limiting, no caching, images and assets served full-size to everyone, payments handled without the protections a payment flow needs, and third-party API bills climbing with every request. We triaged by what was hurting users and costing money, and shipped the most impactful fixes within 24 hours, then worked down the rest.

A founder running a Shopify CRM built on Lovable

The client

A founder running a Shopify CRM built on Lovable

Industry
SaaS for Shopify merchants

Challenge

Fine for the founder and his friends. Not for paying users.

The product worked in the only conditions it had ever seen: its founder and a handful of friends with Shopify stores, poking at it one at a time. Opened to real, paying users, it came apart, because none of the fundamentals were there. Nothing limited how often a user or a runaway loop could hit the backend, so the third-party API bills climbed with every request. There was no caching, so the same expensive calls were paid for again and again. Images and assets shipped at full size to every visitor, and payments were handled without the checks a payment flow needs.

A live product with more than 50 people paying $10 a month to use it, and a founder who is not an engineer watching it break and its running costs climb at the same time. Every new user made both problems worse, so waiting was not an option — the users were paying for something that was failing them now.

Solution

Fix what is hurting users and costing money first — in 24 hours

We triaged by impact and cost, not by tidiness. Rate limiting went in first, so one user or a loop could no longer run up the bill or take the service down. We put caching in front of the repeated expensive calls, so the same work stopped being paid for over and over, and compressed the images and static assets so pages stopped hauling full-size files to every visitor. The payment handling was reworked to add the protections it had been missing. The fixes that were actually hurting paying users shipped within 24 hours; the rest followed in order.

When a paid product is live and failing, the first job is to stop the bleeding — the failures reaching users and the costs leaking money — inside a day. Everything else is scheduled behind that. The founder saw the order and signed off on it before we touched anything.

Calls we would still defend

  • Rate limiting where there was none, so a single runaway user or loop can no longer run up the third-party bill or knock the service over.

  • Caching in front of the repeated expensive calls, so the same request stops being paid for again and again.

  • Image and asset compression, so pages stopped shipping full-size files to every visitor.

  • Payment handling reworked to add the protections a real payment flow needs and the prototype never had.

  • The most impactful fixes went live within 24 hours, because the users were paying and the product was failing them right then.

Results

The paying users had a working product again

Before

A SaaS that passed a friends-and-family test and fell over on real users — no rate limiting, no caching, uncompressed assets, payments without proper checks, and third-party bills climbing with every request.

After

The failures reaching paying users fixed inside 24 hours — rate limiting, caching and compression in place, payments handled properly, and running costs that stopped climbing with every request.

FAQ

The questions this one raises

What was actually built, the constraints it had to meet, and what we would do differently.

  • Yes, and a real paying product — kept anonymous because the client asked us to. We can describe what we found and what we changed, without naming the company or the app.

  • By fixing the right things first. On a live product we triage by what is hurting users and what is costing money, ship those inside a day, and schedule the rest behind them. It is not the whole job in 24 hours — it is the part that stops the bleeding.

  • Very likely. A friends-and-family test never produces the load, the odd inputs or the concurrency real users do. The gap is almost always the fundamentals underneath — rate limiting, caching, and the handling of payments and assets — which is exactly what this rescue put in.

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