The four levels of the C4 model
Context (C1): The big picture
The Context diagram provides a crucial high-level overview of a system, highlighting interactions between software and its environment. This perspective is essential for digital product teams to define system boundaries and external dependencies such as user interactions, third-party integrations and regulatory requirements. For example, in a multi-tenant SaaS platform, the diagram can depict user groups, external identity providers (like OAuth and LDAP), payment gateways (such as Stripe and PayPal), and regulatory systems (including GDPR-compliant services). This clarity aids in addressing strategic concerns around security and compliance, ensuring that non-technical stakeholders—like product managers, compliance officers and legal teams—grasp the system's broader operations. By identifying security needs and compliance requirements (e.g., PCI-DSS, GDPR), assessing risks linked to external dependencies and aligning both business and technical teams, the Context diagram enhances communication and facilitates effective decision-making.
Container (C2): System structure and deployment
At the Container level, the focus is on essential system components like web applications, APIs, databases and services. This layer is crucial for cloud-native applications, helping product leaders understand how architecture is deployed across diverse environments—whether through microservices in Kubernetes, serverless functions in AWS Lambda or databases in managed services like Amazon RDS. It clarifies architectural patterns including microservices and monolithic systems and is vital for teams adopting Continuous Integration/Continuous Deployment (CI/CD) and DevOps practices, where deployment frequency and fault tolerance depend on effective container interaction and scaling. Managers can utilise this framework to plan deployment strategies, ensuring service availability with load balancing and redundancy. Additionally, understanding inter-container communication is key for managing API contracts, service orchestration with tools like Istio or Linkerd and observability through distributed tracing via OpenTelemetry. Lastly, containers empower product managers to assess scalability, aiding teams in planning how services will scale based on load demands.
Component (C3): Detailing the building blocks
The Component diagram decomposes each container into smaller, specific functional units, often in line with Domain-Driven Design (DDD) principles. This method effectively maps services based on business domains like User Authentication, Order Processing, or Data Analytics. By clarifying service boundaries, the diagram helps IT and product managers discern tight and loose couplings, guiding decisions on synchronous APIs versus asynchronous messaging
systems, such as Kafka or RabbitMQ. It fosters collaboration between technical stakeholders, like architects and engineers, and business stakeholders to define clear ownership and responsibilities. This collaborative effort allows product teams to work in parallel, reducing dependencies, enhancing testability, and facilitating smoother agile sprints. Furthermore, by promoting modularization and decoupling, each component can maintain a well-defined purpose, minimizing technical debt and adhering to the Single Responsibility Principle (SRP). Such clarity also supports continuous integration practices, enabling teams to focus on unit and integration testing for individual modules without jeopardizing the entire system's integrity.
Code (C4): A closer look at implementation
The Code level of the C4 Model dives into the intricate details of classes, methods, and their interactions. Although this level doesn’t usually produce diagrams—often overlapping with traditional object-oriented design—it is vital for ensuring architectural consistency across development teams. Key architectural patterns like Command Query Responsibility Segregation (CQRS), event sourcing, or hexagonal architecture should be evident in the code structure, emphasizing maintainability and scalability. To prioritize code quality and reduce technical debt, adhering to clean code principles and design patterns such as Factory, Repository, and Strategy is crucial, especially for large-scale projects. Leveraging automated static analysis tools like SonarQube can help monitor compliance with these principles. Additionally, structuring code for scalability and modularity allows teams to better manage long-term technical debt, with diagrams aiding in identifying areas for refactoring. This level also streamlines developer onboarding and enhances code reviews, as mapping implementation details to the architectural blueprint helps teams quickly identify divergences or violations of guidelines.