Microservices

The alternative to microservices is a monolith: one codebase, one deployment, one database. Early in a project, that simplicity is genuinely useful. A small team can hold the whole system in their heads. Deployment is one command. Many successful systems run this way and never need anything else.

Microservices breaks the monolith into separate services, each responsible for one business domain: user accounts, payments, notifications, inventory. Each service runs as its own process and communicates with others through APIs. When the payment service needs to check a user's account, it sends an API request rather than querying a shared database. This is what microservices.io defines as an architectural style that structures an application as a collection of small, autonomous services.

What makes the pattern valuable is independent deployability. The billing team deploys when their code is ready. The search team does the same. No release windows to align, no waiting for other teams to finish. Each service owns its own data too, so a schema change in one service cannot break another. The practical implications of how services communicate over APIs are covered in API Integration, and the cross-layer thinking this requires connects to how Full-Stack Development teams structure their work.

Shared codebases tend to create friction as teams grow. Merge conflicts multiply, changes in one area break something unrelated, and releases require cross-team synchronisation. Development slows as headcount grows, which is the opposite of what organisations expect.

Amazon reorganised around this problem. The company restructured its engineering so every team communicates only through service APIs, a model its engineers call "you build, you run it", documented by Martin Fowler in his foundational article on microservices. No shared databases, no cross-team release coordination. Teams ship when their service is ready, not when every other team is ready. The deployment velocity people associate with Amazon comes from this structural separation.

Scaling works differently with microservices too. A monolith scales as a unit: if search spikes during a sale, you provision more of everything, including the checkout process that sits idle. With separate services, you scale only what is under load. Lyft runs over 100 microservices on AWS for this reason, each scaling in response to real demand rather than proxying the load of neighbouring services.

It is worth being clear about when this matters. A small team on an early-stage product will nearly always move faster with a well-structured monolith. The coordination and scaling benefits of microservices materialise when the problems are genuine, not when they are anticipated.

The most common mistake in service design is organising by technical function rather than business domain. A database service or logging layer does not actually separate concerns. Domain-Driven Design's concept of bounded contexts is more useful: identify where your business naturally divides, such as orders, inventory, and customer accounts, and draw service boundaries there. These tend to correspond to how teams already think about ownership.

Data ownership matters as much as code ownership. In a monolith, all code reads from one database, so a schema change anywhere can break something everywhere. In a properly structured microservices system, each service has its own data store and other services can only access that data through the owning service's API. This is what makes independent deployment real: you can ship a service without checking whether dozens of other queries will break.

Failure is a design input, not an afterthought. Network calls between services fail. Slow downstream services hold threads open, and one saturated dependency can stall request queues in every service that calls it. Circuit breakers help by stopping calls to a failing service rather than letting requests queue up waiting for a timeout. The DevOps practices around automated deployments and rollbacks make the whole system more resilient when services do fail.

Every service needs its own deployment pipeline. Shared pipelines that bundle multiple services together recreate the coordination problem microservices were supposed to solve. Authentication between services also needs a stateless mechanism. JWT (JSON Web Token) is the standard approach, since it lets services verify identity without sharing session state.

Observability is not optional. Distributed tracing, centralised logging, and health endpoints should be part of the first service you build, not added after the first production incident. When a request fails after touching a dozen services, you need to know where it broke without checking each service's logs separately.

Every service is an operational unit. Each one needs its own pipeline, its own logs to ship somewhere, its own database to back up. Teams that were managing one production system suddenly manage twenty. This is workable with strong automation and clear ownership boundaries, but it is real overhead that does not exist in a monolith.

The failure modes are also different. In a monolith, a bug fails a function. In a distributed system, a slow service holds threads open in every service upstream from it. If that backs up far enough, cascading failures can spread across services that have nothing wrong with their own code. Data consistency gets harder too: there is no distributed transaction that rolls back across service boundaries if something fails partway through an operation. Solutions like saga transactions and event-driven queues address this, but they add implementation surface.

The worst outcome is not a failed migration. It is a distributed monolith: all the operational overhead of microservices, none of the deployment independence. Services that cannot ship without coordinating across four other teams have not separated anything. The coupling has moved from the database to the deployment process. This almost always comes from decomposing a system before the domain was understood well enough to draw boundaries that would hold.

The Technical Debt from wrong service boundaries is expensive to repay. In a monolith, bad decisions accumulate in one codebase. In microservices, they are distributed across multiple services, multiple teams, and multiple databases. Fixing a boundary problem typically means rewriting services and migrating data at the same time, with no clean place to stop.

At Digital Bunch, we have designed microservices architectures for clients in fintech, mobile app development, and enterprise software. These are environments where deployment independence and targeted scaling have direct business consequences, and where getting boundaries wrong creates problems that take months to undo.

Our process starts before implementation. Before any code is written, we map business domains, define service ownership, and draft API contracts between services. We test proposed boundaries against realistic scenarios: if shipping a common user flow requires coordinated changes across three or four services, the decomposition is wrong.

Observability goes in with the first service: distributed tracing, centralised logging with correlation IDs, per-service deployment pipelines, and health endpoints connected to automated recovery. For teams inheriting existing monolithic architectures, we identify which parts are generating actual coordination or scaling pain, then extract those services first. Wholesale migrations that try to decompose everything simultaneously rarely reach completion.