API Gateway, Strangler Fig, CI/CD: Master microservices architecture and lead your migration from the monolith. Complete guide and Smile feedback.
Many organizations embark on a migration to microservices believing they will solve their scalability and deployment velocity problems. Some succeed, but others end up with a distributed system that is just as slow to scale as their original monolith, but infinitely more complex to operate.
The difference between the two rarely lies in the technology. It lies in how the development teams approach the design and the clarity of the decisions made before the first line of code.
This guide provides you with the fundamentals, key patterns and a concrete method for conducting a successful migration.
- Organizations that adopt a well-designed microservices architecture reduce their deployment time per feature from several weeks to a few hours.
- According to industry studies, over 60% of microservices migrations encounter significant problems related to unanticipated distributed complexity.
What is a microservices architecture?
A microservices-based architecture breaks down an application into a set of small, independent, and loosely coupled services. Each service is responsible for a specific business function, has its own database, exposes new features via APIs, and can be deployed, updated, and scaled independently of the others.
This is the opposite of monolithic architecture , or traditional monolithic architecture.
Monolithic vs. microservices: the fundamental differences
Dimension | Monolith | Microservices |
Deployment | A single artifact | One artifact per service |
Database | Shared | One per service |
Scalability | Overall horizontal | Per service as needed |
Coupling | Strong | Weak |
operational complexity | Weak | High |
Start-up time | Slow on a large scale | Fast service |
Debugging | Simple | Complex (distributed) |
The real advantages
Three advantages justify the investment in a distributed architecture.
Independent scalability is the first advantage. You can scale only the service that is under pressure, without affecting the rest of the application. This is a significant cost saving compared to a monolithic application where the entire system must be scaled.
Independent deployment is the second. Each team can deliver its new features to production without coordination with others, which is the basis of continuous delivery.
Resilience is the third. The failure of one service does not necessarily lead to the failure of the entire system, provided that resilience patterns have been correctly implemented.
The often underestimated drawbacks
Distributed complexity is the main pitfall. Network calls between services introduce latency, timeouts, and cascading failures that a monolithic system doesn't experience. Data consistency between services is difficult to guarantee. The observability of a distributed system is radically more complex than that of a monolithic application.
The fundamental patterns of microservices architecture
Domain-Driven Design and bounded contexts
Domain-Driven Design (DDD) is the method that allows for the proper decomposition of an application into services. Each service corresponds to a "bounded context," a business scope consistent with its own data model and vocabulary.
Both an "Order" service and a "Catalog" service can have a "product" concept, but with different attributes depending on their context. DDD formalizes these boundaries and avoids implicit coupling between services.
API Gateway: the single point of entry
The API Gateway is the component that receives all external requests, acts as a load balancer, and routes them to the appropriate services. It centralizes authentication, quota management, logging, and response transformation.
Without an API Gateway, clients need to know the address of each individual service. With one, they only interact with a single entry point, regardless of the internal topology.
Service Mesh: Communication and Resilience
A service mesh is an infrastructure layer that manages communication between services. Istio and Linkerd are the two leading open-source options. They automatically handle communication encryption (mTLS), load balancing, retries, timeouts, and traffic metric collection.
Without a service mesh, each service must implement these mechanisms itself. With a service mesh, the resilience logic is externalized to the infrastructure.
Event-driven architecture: asynchronous decoupling
Rather than directly calling another service via a REST API, a service publishes an event on a message bus (Apache Kafka, RabbitMQ). Interested services subscribe to and react to these events in real time.
This asynchronous decoupling makes services temporally independent: if a consuming service is unavailable, events accumulate in the queue and are processed upon its return. This is a fundamental pattern for building resilient systems.
Circuit breaker: preventing cascading failures
A circuit breaker is a pattern that monitors calls to a remote service. When the error rate exceeds a threshold, the circuit opens and calls to that service are immediately rejected rather than waiting for a timeout.
This prevents slowness or failure of one service from spreading to all dependent services. It is the most important resilience mechanism in a distributed architecture.
Infrastructure as Code and DevOps for microservices
A microservices architecture is only viable in production if the infrastructure that supports it is itself industrialized.
Infrastructure as Code (IaC) is the practice that encompasses configuration management and infrastructure provisioning through versioned and automated code. In a microservices context, each service has its own versioned infrastructure definition in Git and is automatically deployed.
Docker standardizes the packaging of each service into a portable and reproducible container. Each service is delivered with its dependencies, regardless of the target environment.
Kubernetes orchestrates these containers in production. It manages the automatic deployment of new versions, horizontal scaling based on load, high availability, and automatic recovery in case of failure.
Service-specific CI/CD (continuous integration and continuous delivery) pipelines are a non-negotiable requirement. Each service must have its own continuous integration and deployment pipelines, triggered independently of the others. Without this, the benefit of independent deployment is lost. These DevOps practices of continuous deployment and continuous integration are the operational foundation of any production microservices architecture.
Distributed observability is one of the most complex challenges. Logs, metrics, and traces must be centralized and correlated to enable debugging of a request that traverses multiple services. OpenTelemetry is the instrumentation standard, while Jaeger and Zipkin enable distributed tracing.
Migrating from a monolith: strategies and steps
The strangler fig pattern: migrate gradually
The strangler fig pattern is the most proven migration strategy. Rather than rewriting the entire monolith, functionality is gradually extracted to independent services, while keeping the monolith in production.
An API Gateway intercepts requests and routes them to the extracted service or to the monolith, depending on the functionality involved. As data is extracted, the monolith shrinks. When everything has been extracted, it is shut down.
This approach significantly reduces the risk compared to a complete rewrite. It also allows for validation of the architecture incrementally, before everything is migrated.
Identify the right candidates for extraction
We start with the features that combine three characteristics: they have different scalability needs than the rest of the application, they are loosely coupled to the rest of the monolith, and they correspond to a clearly identifiable bounded context.
A notification management service, a report generation service, or an image processing service are often good initial candidates.
Common mistakes to avoid
Trying to migrate everything simultaneously is the most common and costly mistake. It concentrates all the risks on a single delivery and makes reversing the migration extremely difficult.
Underestimating network complexity is the second common mistake. Calls between services that were in-process function calls become network calls with latency, timeouts, and potential failures. This reality must be taken into account from the design stage.
When not to migrate
Migrating to microservices isn't always the right decision. If your team is small, if your application doesn't have differentiated scalability needs per component, or if your monolith is well-structured and easy to deploy, the gain doesn't justify the cost of the migration.
A well-architected modular monolith is often preferable to an undersized microservices architecture.
Smile and microservices architecture
At Smile, we have been designing and deploying microservices architectures since the earliest generations of these patterns. Our expertise covers defining bounded contexts with DDD, designing APIs, setting up service meshes, implementing resilience patterns, and deploying on Kubernetes.
Our approach is pragmatic. We always assess whether a microservices architecture is truly justified before recommending it. When it is, we support the migration implementation gradually, with particular attention to observability and managing operational complexity.
Are you looking to assess the suitability of a microservices architecture for your organization? Discover our DevOps and cloud architecture approach .
Frequently asked questions about microservices
Can a monolith be considered good architecture in 2026?
Yes, absolutely. Monolithic architectures have an undeservedly bad reputation. For small teams, low-traffic applications, or domains without differentiated scalability needs, a well-structured monolith is simpler to develop, deploy, and operate than a distributed architecture.
The current trend towards "modular monolith" reconciles the two approaches: a modular internal architecture that can evolve towards microservices if the need arises.
How many services can a microservices architecture handle?
There is no theoretical limit, but operational complexity increases with the number of services. Large platforms like Netflix or Amazon manage hundreds of services, but with dedicated teams and highly sophisticated tools.
For a standard organization, starting with a dozen well-defined services is wiser than creating fifty from the outset.
What is the difference between microservices and SOA?
SOA (Service-Oriented Architecture) is the ancestor of microservices, popularized in the 2000s. The two approaches share the logic of decomposition into services, but differ on several points.
SOA relied on heavyweight protocols (SOAP, ESB) and a centralized software development approach, with generally larger services. Microservices favor lightweight REST APIs, highly granular services, and DevOps practices of continuous deployment.
How to manage data consistency between multiple services?
This is one of the most complex challenges of distributed architecture. Two main approaches exist: the Saga pattern for distributed transactions (each service executes its transaction locally and publishes an event, with a compensation mechanism in case of failure) and eventual consistency (accepting that data will not be immediately consistent between services, but will be eventually). The choice depends on the business requirements for consistency and performance.
