Compare HTTP Toolkit vs. Testkube

You can easily scan through traffic by highlighting content type, status, and source. Or, you can use powerful filtering tools that match your messages to the most important ones. With MDN inline explanations and docs, you can examine the URL, status, headers, and body of each request, or response. You can explore message bodies using highlighting and auto formatting for JSON HTML, JS, JS and other formats, all with the help of Monaco, the editor in Visual Studio Code. Match requests precisely, jump to them as they appear, and edit any information: the target URL or method, headers, body, or method. You can either respond manually to the requests as they arrive or pass them upstream. On the way back, pause and edit the actual response. Step through HTTP traffic request-by-request, or manually mock errors and endpoints. To quickly prototype against new services or endpoints, create rules that match requests.

A test execution is defined by a combination of a testing tool, a repository of tests, and specific arguments or parameters. You can utilize the out-of-the-box workflows provided by Testkube for various testing tools or create a personalized workflow tailored to your needs. It's possible to group related or unrelated test execution workflows into comprehensive suites for better organization. Additionally, you can establish a searchable repository filled with test executions that are ready to run. Proactive triggers allow you to initiate test executions in response to CI/CD events, on a manual basis, or according to a predefined schedule. Furthermore, you can set up your system to respond to Kubernetes infrastructure events, like deployments, to automatically launch test executions. With Kubernetes’s inherent scalability, you have the option to increase the number of concurrent test executions to meet demand. You can also take advantage of advanced scaling options to enhance load tests, allowing you to expand from a single execution to as many as 1,000. By parallelizing functional tests, you can significantly reduce the time required for completion. All logs and artifacts generated during the execution of test tools are stored in a centralized repository, making them easily accessible. Moreover, the system includes features for quickly identifying errors by highlighting keywords associated with failures in the logs. In addition, utilizing various filtering options can enhance your ability to sift through logs efficiently, ensuring a smoother debugging process.