What a Launch Taught Me About Hidden API Costs
What I learned after a warehouse sale launch exposed hidden third-party API costs, and how using Redis to precompute data helped cut those costs by 50%.
Setup
In the later end of summer 2024, I was one of two engineers building a warehouse sale storefront backed by BigCommerce. Within a short timeframe, we focused on getting the customers through the checkout without friction.
When we launched, traffic arrived, and the site did what it was supposed to.
There was just one problem.
The storefront depended on a third-party inventory API for the availability data, and when 250k visitors showed up, the API was hit harder than I expected. During the first stretch of the public launch, the company behind the inventory integration sent us a bill that was in the thousands.
The fix used a Redis-backed service that pre-computed product payloads and moved that inventory work away from the request path. It was from this experience that I learned to look at third-party services differently once real traffic gets involved.
Why It Happened
During development and even the employee soft launch, the integration looked fine. The problem was easier to see once I traced what had happened during a normal read.
The storefront was pulling a mix of data from BigCommerce and the inventory provider. What I missed at first was that the inventory API wasn't being called once per product, but once per SKU.
This matters because some products had several variants. A single product page view could trigger many inventory requests.
I wouldn't classify this as a normal site failure, but rather too much expensive work was happening on demand, and real traffic exposed that.
What I Changed
I didn't want to re-write the storefront after launching. Instead I focused on changing where expensive work was happening.
I built a small service that:
- pulled category trees and product paths
- fetched product, variant, brand, image, and inventory data
- flattened the result into storefront-ready payloads
- stored those payloads under product and category keys in Redis
This meant the storefront no longer had to orchestrate multiple APIs on every request. Instead of rebuilding the data shape over and over, it would read precomputed data from Redis while the expensive work would happen during the refreshes.
There was a tradeoff: data freshness. The data would become slightly stale between refreshes, but for this storefront it was acceptable. Mostly because the BigCommerce checkout would perform a last minute inventory check to make sure the product was indeed purchasable.
This changed our next bill from the inventory integration. By doing this, I cut the API cost by about 50%.
What I Learned
The main lesson I learned from this was third-party APIs should be evaluated for repeated costs when they deal with requests.
Before this, my main focus was on whether an integration worked and whether I could get the right data back. Now I think much harder about how often a service might get called, what work it triggers under traffic, and whether that work needs to be performed on demand at all.
This project also changed how I think about caching. Removing repeated work from the hot path and moving it into a cheaper, controllable process is much better than just storing responses to make things faster.
I'm carrying this into future projects. The system can be correct, launch successfully, and still hide unhealthy patterns until real traffic exposes it. I'm much less casual about how anything can turn into paid upstream work from normal traffic.
