Mobile checkout abandonment was triple industry average and the shop could not handle Black-Friday peaks.
What they were actually dealing with.
A direct-to-consumer brand doing roughly $8 million in annual GMV had built its storefront on Adalo, Webflow, and Xano. The stack had gotten them to real revenue. It had also produced a mobile checkout that customers were abandoning at triple the industry average rate.
The abandonment problem needed fixing before the brand's biggest single revenue day of the year, Black Friday, which the existing stack had already failed to handle at the previous year's traffic. There was no room to ship a rebuild that hadn't been proven under real peak load before that date arrived.
The decisions that actually mattered.
Not a task list. The four calls that determined whether this worked.
Checkout rebuilt first, storefront second
Rather than migrating the whole platform at once, the highest-abandonment surface, mobile checkout, was rebuilt and shipped first, so the metric that mattered most started improving before the rest of the migration finished.
Django chosen for the inventory and order logic
The checkout speed problem traced partly to Xano's generic query layer against inventory and pricing rules with real complexity. Django's ORM and the ability to hand-tune the specific queries this catalogue needed replaced a one-size-fits-all data layer.
Load-tested against last year's Black Friday traffic, not average traffic
Before cutover, the new checkout was load-tested against the traffic pattern that had actually broken the old stack the previous year, not a generic benchmark, so the test that mattered was the one that ran.
Vercel plus AWS split by workload shape
The storefront runs on Vercel for edge-cached delivery; order processing and inventory run on AWS where the workload benefits from dedicated compute. The split follows what each workload actually needs rather than a single-vendor default.
Is this outcome remarkable or ordinary?
For calibration: mobile checkout abandonment at three times the industry average is a severe outlier — typical e-commerce mobile abandonment already runs high, and a 6x checkout speed improvement paired with a 34% mobile conversion gain reflects a checkout that was structurally broken rather than merely slow.
Source: General e-commerce mobile checkout abandonment benchmarks
“Black Friday was the first test. We did 3.1× last year's revenue without a single timeout — no scramble, no war room.”
If you're in the same position.
When a peak-traffic event has a fixed date, load-test against the specific traffic pattern that broke the old system, not a generic benchmark. A rebuild that passes an average-load test can still fail the one day of the year when it actually matters.
You shipped fast on no-code.
Now ship faster, on code.
30-minute scoping call · pixel-faithful migration plan · fixed-price quote within 48 hours. No NDA gymnastics.