Successful digital businesses design, develop and deploy software quickly without sacrificing security or quality. Although many teams have transitioned to microservices from monoliths as the building blocks of their distributed applications in cloud native environments, that choice is worth looking into more closely to understand why.Microservices are a modern IT architectural model that accelerates software development processes, speeding application time to market. With the widespread adoption of cloud computing, the microservices approach has essentially replaced the monolithic architectural model.Each microservice is an independent software codebase that creates an architecture of many small, modular services. Individual microservices are deployed in containers creating microservices applications. In cloud native environments, a microservice is autonomous and often ephemeral — created, destroyed and refilled on demand — as well as loosely coupled in support of a larger application or process.Microservices, which are typically developed by independent teams, can be run and updated separately without depending on other services. This requires engineering, observability and DevOps teams to have a robust understanding of overall system dependencies and how each team works together to run a cloud native environment.A microservices approach, often correlated with Agile and DevOps principles, empowers developers to be responsive to customers’ changing needs and demands because teams can release new capabilities and bug fixes on a flexible schedule. This is different from a monolithic approach where developers must iterate on a single codebase and deploy all changes as part of a larger release or a release train.Componentized or microservices architectures deliver many advantages to organizations, including:Although a microservices architectural model accelerates development, it can also lead to unanticipated challenges for the organizations and developers creating them. These can include:Before the introduction of cloud computing and subsequently microservices, organizations primarily relied on a monolithic approach to application development. In 2023, a much smaller portion of released applications begin as monoliths where all of the functionality and business logic of a project are joined — the data repository, the persistence layer, the service layer and the presentation layer.A monolithic architecture is a traditional software development paradigm in which developers design and build a single, dependent codebase over a specific period of time — often 18 months or longer — before releasing it into production. All in one, a monolithic architecture combines the business logic of many capabilities — for example, a gaming application’s play and in-app purchasing functions — into one experience and releases of new functionality roll out into production on the same schedule, at the same time.The monolithic architectural approach can be useful to organizations operating applications that are limited in scale and do not primarily serve business-critical functions. These advantages include:A monolithic approach is similar to “putting all of your eggs in one basket” in that it works well until something significant changes — teams need to add new capabilities quickly, scale is needed or performance is degrading. These are some of the disadvantages of a monolithic architecture:The primary difference between microservices and monolithic architectures is many codebases versus one codebase. In microservices architectures, individual services with their own codebases operate independently as part of a distributed system that communicates with the larger application. In contrast, a monolith architecture is one system with a single codebase that operates as one connected, dependent entity.A second major difference between microservices and monolithic architectures is their scalability. Monoliths can be challenging to scale while microservices are built to become part of an application platform that ensures scaling almost limitlessly. A third key difference between microservices and monolithic architectures is their resourcing. Independent teams create and support their own microservices while large, co-dependent teams typically develop and then hand off monoliths.Although the approaches are different, there can be solid business reasons for using both microservices and monolithic architecture in today’s digital environments.Organizations should lean into microservices architecture if they need to:Organizations should stay the course with monoliths if they need to:While accelerating application development, going cloud native has introduced challenges for organizations adopting microservices architectures on container-based infrastructures. With application updates happening multiple times a day, teams also can be challenged to track down problems when error messages appear or when unexpectedly, application loading takes longer.The monolithic architectural approach ensures a straightforward application call, making it easy to find exactly where a problem exists with basic application performance monitoring (APM) software. In contrast, a cloud native app — and its many containers — are ephemeral, which means there are more possible problem sources and locations. That’s why teams embracing microservices need cloud native observability with distributing tracing capabilities. Distributed tracing from Chronosphere shows teams exactly where a problem is occurring so they can quickly fix applications.Why is distributed tracing the key to observing cloud native distributed systems? What used to be a single trackable request within the bounds of a monolith viewable with APM is now a long drawn-out game of telephone where multiple requests are made to services to fulfill a single initial request. What gave us visibility into monoliths — focusing on a single component and the interactions within — only provides a piece of the puzzle in today’s systems.In addition to highlighting issues, distributed tracing captures individual units of work, also known as spans and the relationships/dependencies between services. A workflow request is a distributed tracing example that is a series of activities necessary to complete a task. Although workflow requests may be the same for multiple users — a request for a size — for example, the associated metadata is unique. Analyzing trace metadata is helpful to engineers investigating issues because they can use it to identify patterns, anomalies, or outliers as well as where issues are in a stack.With Chronosphere observability, engineers can:Cloud native observability is built for microservices’ unique monitoring challenges, addressing both metrics’ high cardinality in containerized, cloud native apps and the need to automate as much management overhead as possible.Community created roadmaps, articles, resources and journeys fordevelopers to help you choose your path and grow in your career.