Catching API regressions across microservices before they ship

Most backend teams I've worked with start API testing the same way: Postman collections. Someone builds a collection while developing, it grows, the team shares it, and for a while it's genuinely enough.

Then the team scales, the API grows past a few dozen endpoints, and the cracks show. The collection needs constant manual upkeep. Regression testing means someone remembering to run the collection before a release. And the tests only cover the cases someone thought to add — which is never all of them.

We went through exactly this, and I want to share how we moved from manual Postman testing to automated regression without throwing away the work we'd already done. If your team is at the "our Postman collection is becoming a second job" stage, this is for you.

Why Postman stopped scaling for us

Nothing against Postman — it's excellent for exploration and collaboration. But for automated regression at scale, three things worked against us:

1. Manual upkeep. Every API change meant manually updating requests and assertions.
2. Human-triggered runs. Regression depended on someone remembering to run the collection.
3. Coverage gaps. We only tested the paths we explicitly added — the edge cases that actually caused incidents were rarely in the collection.

The core issue: the effort scaled linearly with the API, and it all sat on people.

What we didn't want to do

The obvious move was "rewrite everything as automated tests." We rejected that — it meant discarding months of collection work and hand-authoring a whole new suite. Same authoring burden, different tool.

What we wanted was to keep the Postman work and automate on top of it.

The migration, in practice

We used Keploy, and the migration had two tracks running in parallel:

Track one — import what we had. Keploy can take an existing Postman collection (or an OpenAPI spec) as input and generate regression tests from it. So our existing collection became the starting point for the automated suite instead of being thrown away.

Track two — record what we didn't have. For everything the collection didn't cover, we recorded real traffic from our running services. That filled the coverage gaps with cases that reflected actual behavior — including the edge cases nobody had thought to add to Postman.

Then we wired the resulting suite into CI so it ran on every pull request, with mocked dependencies so it didn't depend on external services being available. Regression testing stopped being a person's responsibility and became part of the pipeline.

For the full mechanics of how the two approaches compare, this breakdown was useful during our evaluation, and this wider API testing tools comparison helped us see where each tool fits.

The outcome

• Regression testing moved from "someone runs the collection" to "every PR is checked automatically"
• Our existing Postman work carried forward instead of being rewritten
• Coverage went up because recorded traffic caught cases manual authoring had missed
• The maintenance burden dropped — the suite reflects real behavior, so it needs less hand-tuning as the API evolves

The lesson for other teams

Migrating off manual Postman testing isn't about abandoning Postman — it's about not depending on manual collection maintenance for something as critical as regression coverage. Keep the collection for exploration. Automate the regression layer from your existing inputs and real traffic.

If your backend team is feeling the manual-testing tax, that's the transition worth making. Happy to compare notes with anyone going through it — this is a common inflection point and there's no reason to do it the hard way.