Microservices-Based Distributed Architectures

What are microservices

Microservices are an architectural style that decomposes an application into small, well-bounded, independent services. Each service encapsulates a specific business capability —such as catalog, inventory, billing, or payments— and can evolve, deploy, and scale independently, reducing coupling between components.

In a monolithic architecture, a change in one module can increase operational risk and affect the stability of the entire application. In a well-designed microservices architecture, changes are isolated, reducing their impact on the rest of the system and enabling more frequent delivery cycles.

When microservices make sense

Adopting microservices is usually advisable when:

• Multiple teams work simultaneously on the same system.
• Different parts of the application have very different scalability requirements.
• Current deployment cycles are slow or high-risk.
• Different business domains require different technologies or evolution paces.

When we do not recommend them

Not every application requires a microservices architecture. We do not recommend this approach when:

• The product is small and has limited evolution ahead.
• The development team is small.
• The operational complexity outweighs the expected benefits.
• A modular monolith adequately solves the problem.

In those cases, a well-designed monolith usually offers a better cost-benefit ratio, and that is what we advise.

Our methodology

Domain analysis. Applying Domain-Driven Design principles, we analyze the business domain, identify bounded contexts, and define the boundaries of each service.

Design. We define the API Gateway, data model, security, and infrastructure based on each project's requirements. We design stable APIs and contracts between services to minimize coupling and enable the independent evolution of each component.

Phased extraction (Strangler Pattern). Services are incorporated progressively while the existing system continues to operate, reducing migration risk and delivering value from the early phases.

Continuous integration and delivery. We automate the full cycle through CI/CD pipelines, automated testing, and progressive deployment strategies that reduce operational risk.

Observability. We implement distributed tracing, metrics, centralized logging, and alerting to reduce diagnosis times and support ongoing operations.

Service communication and integration

Inter-service communication is designed according to each use case. When the scenario requires it, we use event-driven architectures with brokers such as Kafka, RabbitMQ, or equivalent managed services, which decouple services over time and manage eventual consistency in a controlled way.

Runtime infrastructure

Microservices architecture is a software design decision. Kubernetes is one possible runtime platform, but not a requirement for adopting this approach: we also work with ECS, Cloud Run, on-premise, and hybrid environments, according to each organization's needs.

Modernization and new products

Many organizations run legacy systems that work but limit their ability to evolve. In most cases, it is not necessary to fully replace an existing application: incremental migration allows the current system and the new services to coexist throughout the process.

We also design native microservices-based architectures for new products, avoiding carrying the limitations of legacy systems into new developments.

Common mistakes we avoid

During a migration, it is common to find design decisions that increase complexity without adding value. Among the most frequent are:

• Excessively fragmented services relative to the actual problem size.
• Databases shared across domains.
• Lack of observability and distributed tracing.
• Excessive synchronous communication between services.
• Missing resilience mechanisms against partial failures.
• Inconsistent security models across services.

Technologies we work with

Docker, Kubernetes, OpenShift, AWS ECS, Azure AKS, Google GKE, Kafka, RabbitMQ, Redis, PostgreSQL, SQL Server, MongoDB, Elastic, Prometheus, Grafana, Jaeger, and OpenTelemetry, among others.

For deeper analysis and case studies, visit our Insights section.