Discussions

Testing in production often sounds scary, but it’s becoming a common practice in modern software teams. With complex systems, distributed microservices, and rapid release cycles, some issues just can’t be caught in staging or pre-production. That’s where testing in production steps in—but it has to be done carefully to avoid disrupting users.

One best practice is to use feature flags or toggles. This allows teams to release new functionality to a small subset of users, monitor the results, and gradually roll out to everyone. It reduces the blast radius if something goes wrong.

Another important approach is canary deployments. Instead of pushing changes to the entire system, you deploy them to a limited number of servers or regions first. This helps validate performance and functionality under real-world conditions.

Observability and monitoring are also critical. Real-time logging, tracing, and alerting give teams immediate feedback if errors or anomalies appear. Without proper monitoring, testing in production becomes guesswork rather than controlled experimentation.

Finally, it’s essential to simulate realistic traffic before exposing features broadly. Tools like Keploy can help here by generating production-like API tests and mocks, ensuring that your code behaves consistently even as it interacts with other services.

At the end of the day, testing in production isn’t about recklessness—it’s about pragmatism. When combined with safeguards like automated rollbacks, alerting, and gradual rollouts, it can actually make systems more resilient and reliable.

So the key takeaway? Treat testing in production as a carefully managed extension of your QA strategy, not a shortcut. With the right practices, it can become a powerful ally in delivering faster and safer software.