Comparing monolith and microservice architectures for software delivery

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 wide-spread 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 any dependence on other services. This requires engineering, observability, and DevOps teams to have a robust understanding on 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:

• High reliability and scalability: With microservices, teams can scale independent services focused on a specific problem and manage that experience in collaboration with their operations teams through automation and patterns.
• Faster time to market: Teams working on applications with evolving feature sets can use microservices’ modularity to their advantage, separating services and automating the delivery of some capabilities to production faster than others (even many times a day) to meet evolving business requirements.
• Efficient resource usage: Because microservices are independent, organizations can scale out specific microservices at their own pace and without having to scale out a full application. Teams also have the freedom of choice to use their own technology stacks to create the microservices.
• More productive teams: Smaller teams typically create and quickly deploy microservices, making organizations more agile and employees more productive.

Although a microservices architectural model accelerates development, it can also lead to unanticipated challenges for the organizations and developers creating them. These can include:

• Complexity: Many teams working independently on microservices can lead to new complexity and higher costs in the development lifecycle. If sprawl and performance degradation occurs, teams may find finger-pointing occurring across organizations.
• Higher costs and operational overhead: Individual teams need their own systems, software, infrastructure, and operational processes to be successful, which can lead to higher costs for both equipment and top talent.
• Observability challenges: Although application components are simpler in microservices architectures than monoliths, request workflows are more complex. That can make discovering issues harder in a microservices architecture. Tracking a request through the complex and expanding architecture of microservices in your organization is one of the most technically challenging aspects of your cloud native observability solution.
• Unicorns: Lack of standardization across teams and systems can lead to organizations having to maintain unique environments to support a wide variety of applications.

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 code base 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:

• Simplified development, testing, and deployment: Teams build and test monolithic applications on consistent infrastructure with similar tools as a single code base, and then deploy these applications as one release into production.
• Consistent performance: Because teams rely on the same tools and processes to build monolith after monolith, they can guarantee consistent performance.
• Fast issue resolution: Despite application components being more complex in a monolithic architecture than a microservices ones, workflow requests are easier to follow, allowing teams to discover issues as they happen.

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:

• Slower time to market: Because all of the code must be designed, developed and deployed in specific steps and as one package, it can take longer to get new capabilities released to market that meet the demands of customers or employees. This tightly coupled integration across monolithic application components makes it very hard to change any aspect of the solution and only gets worse the longer the lifespan of the monolith.
• Dev vs. Ops challenges: Development teams work closely together on releases of monoliths until they move into production when the software is handed to the operations team for long-term support, including bug fixes and performance.  As the code grows, it becomes harder to manage and time-consuming to get troubleshooting resources up to speed.
• Scaling obstacles: Monolithic architectures have tightly coupled software components that are dependent on one another to operate. Efficient scaling is nearly impossible. When a component slows or breaks, the whole application may stop functioning properly.

The primary difference between microservices and monolithic architectures is many code bases versus one code base. In microservices architecture, individual services with their own code bases 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 code base 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:

• Rapidly release software features (as a new or into an existing app): Microservices speeds time to market by allowing developers to work independently in parallel on new capabilities, and quickly bring those services together at once into production.
• Scale an application while enjoying freedom of choice: Teams can use the tools they want to and need to rapidly add many microservices and scale an application to meet unexpected and/or fluctuating demands.
• Boost reliability and fault tolerance: Because microservices are distinct pieces of code, applications can keep working even if one service fails.

Organizations should stay the course with monoliths if they need to:

• Update a non-business critical application: Teams can work on the same code, focusing their delivery of new features for legacy systems on a time-bound schedule.
• Lower latency: Monoliths communicate in one way and microservices in many, requiring more time for packets to cross networks.
• Reduce technical headcount: Creating and delivering more than one code base can be costly and time-consuming for development organizations.

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:

• Seamlessly jump from an alert or a dashboard into related traces. From there, they can quickly see where the source of a problem lies.
• Easily visualize how errors in a microservice are impacting services upstream in the request.
• Request a statistical analysis of all of the traces in a time span to compare them to traces in a time span a few minutes prior to the incident and spot differences.
• Quickly discover where a problem is, so it can be fixed and the business can get back on track.

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.