Architecture patterns are not academic exercises. They are practical solutions to problems that every web application encounters at some point: how to organize code so multiple developers can work without stepping on each other, how to scale specific parts of the system without rewriting everything, and how to keep the application maintainable as features pile up over months and years.
You do not need to memorize every pattern in a software architecture textbook. But understanding these five patterns, what problems they solve, when they help, and when they add unnecessary complexity, gives you a stronger foundation for making design decisions on real projects. Here are the five patterns I keep coming back to.
1. Model-View-Controller (MVC)
MVC is the oldest pattern on this list and the one most developers encounter first. It separates an application into three layers: the Model (data and business logic), the View (what the user sees), and the Controller (the logic that connects user actions to data changes).
Nearly every server-side web framework uses some version of MVC. Django calls it MTV (Model-Template-View), Rails follows classic MVC, and Express.js gives you the building blocks to implement your own version.
MVC works best for applications with clear CRUD operations: admin panels, content management systems, e-commerce backends, and internal tools. The pattern breaks down when your application has complex, multi-step user interactions that do not map cleanly to "read a model, render a view, update a model" cycles. Real-time collaborative features, multi-step wizards with branching logic, and applications with heavy client-side state often outgrow MVC quickly.
The key insight from MVC is separation of concerns. Even if you never use a strict MVC framework, keeping data logic, presentation logic, and routing logic in separate layers makes your code easier to test and modify.
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2. Microservices
Microservices split a monolithic application into independently deployable services, each responsible for a specific business capability. The user service handles authentication and profiles. The payment service handles transactions. The notification service handles emails and push notifications. Each service has its own database, its own deployment pipeline, and can be written in a different language if needed.
The appeal is obvious: independent scaling, independent deployment, and teams that can work autonomously on their service without coordinating deployments with every other team. Martin Fowler's guide on microservices remains one of the clearest explanations of the pattern and its trade-offs.
The hidden cost is equally significant. Microservices introduce network latency between every service call, require distributed tracing to debug issues, need service discovery and load balancing infrastructure, and make local development significantly more complex. A developer who needs to run five services locally to test a single feature spends more time on environment setup than on the feature itself.
The honest rule of thumb: if your team has fewer than 20 developers and your application handles fewer than 10,000 requests per second, a well-structured monolith will serve you better than microservices. You can always extract services later when specific scaling needs emerge. Going the other direction, from microservices back to a monolith, is much harder.
3. Event-Driven Architecture
Event-driven architecture (EDA) structures communication around events: things that happened. Instead of Service A directly calling Service B, Service A publishes an event ("order.placed"), and any service that cares about that event subscribes to it and reacts independently.
This pattern decouples services more effectively than direct API calls. The order service does not need to know that the notification service, the analytics service, and the inventory service all care about new orders. It just publishes the event, and each subscriber handles its own logic.
Message brokers like RabbitMQ, Apache Kafka, and cloud-native options like AWS EventBridge or Google Cloud Pub/Sub provide the infrastructure for event-driven systems. The CloudEvents specification offers a standardized way to structure events across different systems.
EDA shines in systems with complex workflows that span multiple services: e-commerce order fulfillment, financial transaction processing, IoT data pipelines, and any system where multiple independent processes need to react to the same trigger. The trade-off is eventual consistency. When Service A publishes an event and Service B processes it asynchronously, there is a window where the two services have different views of the data. Building your application to handle this gracefully requires careful design.
"Architecture decisions should be driven by the real constraints you are facing today, not the constraints you might face in two years. The best architecture is the simplest one that handles your actual requirements." - Dennis Traina, 137Foundry
4. Command Query Responsibility Segregation (CQRS)
CQRS separates read operations (queries) from write operations (commands) into distinct models. Instead of a single data model that handles both reading and writing, you maintain a read-optimized model and a write-optimized model, often backed by different data stores.
This pattern solves a specific problem: when the shape of data you need for display is fundamentally different from the shape of data you need for processing business logic. A product catalog that needs to display denormalized data with aggregated reviews, pricing tiers, and availability across warehouses benefits from a read model that pre-computes these views. The write model, meanwhile, can enforce strict business rules and maintain normalized data integrity.
CQRS adds complexity. You now have two models to maintain, a synchronization mechanism between them, and potential consistency delays. The Microsoft documentation on CQRS provides a thorough analysis of when the pattern justifies its overhead.
Use CQRS when your read and write patterns have genuinely different performance characteristics or data shape requirements. A reporting dashboard that aggregates data from multiple sources is a natural fit. A simple CRUD application with straightforward reads and writes gains nothing from the added complexity.
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5. Serverless Architecture
Serverless architecture runs your application code in stateless functions that execute on demand. You do not manage servers, containers, or scaling infrastructure. The cloud provider handles all of it, and you pay only for actual execution time.
AWS Lambda, Google Cloud Functions, and Azure Functions are the major serverless platforms. Serverless is strongest for applications with variable traffic patterns: an API that handles 10 requests per minute most of the day but spikes to 10,000 during a marketing campaign scales automatically without manual intervention.
The limitations are real. Cold start latency (the delay when a function has not been invoked recently) can add 100ms to several seconds depending on the runtime and package size. Long-running processes that exceed the platform's timeout limit (typically 5-15 minutes) need to be restructured or moved to a different compute model. Debugging distributed serverless systems is harder than debugging a single application server.
Serverless works best as a complement to other patterns rather than a wholesale replacement. Use serverless for webhooks, scheduled tasks, image processing, and API endpoints with unpredictable traffic. Keep your core application logic in a traditional server or container if it requires persistent connections, long-running processes, or predictable latency.
Choosing the Right Pattern
No pattern is universally correct. The architecture that works for your project depends on your team size, your scaling requirements, your deployment infrastructure, and how your application's complexity is distributed.
Start with the simplest architecture that meets your current requirements. A well-structured monolith with MVC is the right starting point for most new applications. Add event-driven communication when you need to decouple workflows. Consider CQRS when your read and write patterns genuinely diverge. Extract microservices only when a specific part of the system needs independent scaling or independent deployment cadence.
For more detail on evaluating your full technology stack, including how architecture patterns interact with framework and database choices, this guide covers the complete decision framework. Teams at 137Foundry often help clients match architecture patterns to their real-world constraints before any code gets written.
The goal is not to learn every pattern. It is to understand which problems each pattern solves so you can reach for the right one when the need arises.
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