As development teams transition from monolithic applications to microservice-based architectures, they increasingly take ownership of deployment and production operations, emphasizing the need for service reliability. This shift poses challenges in ensuring services are dependable, particularly as these microservices, often implemented with containers and orchestrators like Kubernetes, need to operate independently and be resilient to failures. Services are defined as a set of functionalities provided by systems, and their reliability is crucial because a failure in one can cascade into broader application issues. To measure service reliability, teams should focus on the Four Golden Signals—latency, traffic, errors, and saturation—while tools like Gremlin offer automated tests to assess and improve service reliability by simulating failure scenarios. As control shifts to developers, integrating reliability into workflows becomes essential to demonstrate service dependability to users and stakeholders, leveraging platforms like Gremlin to proactively identify and mitigate availability risks before they impact user experience.