API Testing for Builders
Clicking through the same login flow by hand after every deploy does not scale, and neither does trusting an API "probably still works." This course teaches you how to build a real request collection, script assertions that actually catch regressions, automate it in CI so a broken endpoint fails the build instead of reaching production, and mock an API before the real backend exists. By the end you will have a real, automated API test suite running against a real project.
What you'll learn
Course outline
Free — no account needed
Why Manual API Testing Stops Working
It works fine for one endpoint — it breaks down the moment you have thirty
How API Clients and Collections Work
Requests, environments, and variables — the building blocks behind every API client
Your First Scripted Assertion
Stop eyeballing the response — write a test that checks it for you
Full course — $59 one-time
Choosing an API Testing Tool
Postman, Insomnia, Bruno, Thunder Client — what actually differs once you look past the UI
Building a Real Test Collection
Organizing requests, chaining data between them, and testing more than just the happy path
Automating API Tests in CI
A test suite that only runs when someone remembers to click it is not actually automated
Mocking APIs Before the Backend Exists
Let frontend and backend teams build in parallel against an agreed contract
The Production API Testing Checklist
Coverage, flakiness, and the details that determine whether anyone actually trusts the suite
Get the full course
8 lessons — from your first scripted assertion to a CI-automated, mocked test suite.
About this course
Manually clicking through the same API requests after every deploy works for a handful of endpoints, but breaks down the moment a shared change can quietly affect ten unrelated routes nobody thinks to re-check by hand. Learning API testing means understanding how request collections and environments work across tools like Postman, Insomnia, and Bruno, how to write scripted assertions that replace eyeballing a response with a specific pass/fail check, how to chain data between dependent requests in a real workflow, and how to automate a collection in CI so a broken endpoint fails the build instead of reaching production.
This course is for developers who currently trust an API "probably still works" rather than having a test suite that proves it. After completing it you will be able to write a scripted assertion that actually catches a regression, choose between Postman, Insomnia, Bruno, and Thunder Client based on your team's real collaboration needs, build a chained collection covering both happy-path and failure cases, automate that collection in CI with Newman so a failing test blocks a merge, and mock an API before the real backend exists so frontend and backend teams can build in parallel.
Frequently asked questions
Do I need a dedicated API testing tool, or is manual testing in the browser enough?
Manual testing is reasonable for your first few endpoints, but it does not scale — it cannot be automated to run on every deploy, it is not repeatable across a team without everyone re-creating their own ad-hoc requests, and it realistically only covers the happy path since manually testing every error code and edge case by hand is not practical. A real test collection with scripted assertions solves all three problems at once.
What is the difference between Postman, Insomnia, and Bruno?
They all send requests and run scripted assertions, but differ in where collections live and how deep the collaboration features go. Postman and Insomnia store collections in their own cloud by default with mature team features; Bruno stores collections as plain text files directly in your Git repo, reviewed via pull request like code — the right choice if full data ownership matters more than built-in collaboration tooling.
How do I run my API test collection automatically instead of clicking it manually?
Export the collection and run it in CI using a CLI runner — Newman for Postman collections is the most common path, run as a step in GitHub Actions or another CI provider against a dedicated staging environment. Configure the step to fail the build when any assertion fails, so a broken endpoint blocks the deploy instead of just logging a warning that gets ignored.
What is API mocking and when do I actually need it?
A mock server returns realistic, predefined responses for an API that does not exist yet, or that you do not want to call repeatedly — a third-party API with cost or rate limits, for example. It is most valuable when frontend and backend teams are working on different timelines and need to build against an agreed contract before the real backend exists, or when you need to reliably simulate an error response a real third-party API will not fail on demand.
Should my test collection only check successful responses?
No — a collection that only tests success cases gives false confidence, since most real production bugs happen on error paths, not the happy path. Deliberately test invalid input, missing authorization, and edge-case values like empty strings or very large numbers alongside the happy-path requests, so the suite actually verifies how your API behaves when something goes wrong, not just when everything goes right.