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10 Software Architecture Patterns You Must Know About

Israr Ahmed

Nov 12, 2025 • 10 min read

Software architecture patterns diagrams

Architecture is the blueprint of your software. From monoliths to microservices, each pattern shapes how your system scales, recovers, and evolves. Picking the right one early can prevent costly rewrites later.

This guide breaks down ten proven patterns, showing how they work, why teams use them, and when each shines. Use it as a starting point to align technical decisions with your product roadmap.

Layered (N-Tier) Architecture

Organizes application logic into horizontal layers — presentation, business, data access, and persistence — each focused on a dedicated responsibility.

When it fits best

  • Enterprise systems with well-defined workflows
  • Teams working on separate code modules
  • Projects prioritizing maintainability over rapid scaling

Pros

  • Clear separation of concerns
  • Developers can focus on a layer expertise
  • Easier testing and maintenance

Cons

  • Layer coupling can grow over time
  • Scaling requires duplicating entire layers
  • Slower to adapt to changing business rules

Real-world example

Traditional CRM or ERP where UI, business rules, and data persistence are handled in distinct layers.

Event-Driven Architecture

Components communicate by producing and consuming events. Services react asynchronously when significant state changes occur.

When it fits best

  • Real-time analytics and monitoring
  • IoT and messaging platforms
  • Asynchronous transaction workflows

Pros

  • High decoupling and flexibility
  • Easy to introduce new consumers
  • Scales naturally with demand

Cons

  • Harder to trace event flow for debugging
  • Requires event broker infrastructure
  • Needs idempotency and failure handling strategies

Real-world example

An ecommerce checkout publishes OrderPlaced, triggering fulfillment, invoicing, and email services independently.

Microservices Architecture

Breaks an application into independent services, each owning a single business capability and communicating via lightweight APIs.

When it fits best

  • Large products with autonomous teams
  • Systems needing independent scaling and deployment
  • Organizations embracing DevOps and CI/CD

Pros

  • Independent deployability
  • Fault isolation
  • Polyglot persistence and technology freedom

Cons

  • Complex distributed operations
  • Requires mature monitoring and observability
  • Potential consistency challenges

Real-world example

Netflix services for playback, recommendations, profiles, and billing all deploy separately and scale on demand.

Monolithic Architecture

All components live in a single code base, sharing memory and resources, and deploy as a single unit.

When it fits best

  • MVPs and early-stage startups
  • Small teams wanting fast iteration
  • Projects with limited scope

Pros

  • Simple to develop, test, and deploy
  • Easier to understand for new team members
  • Lower infrastructure overhead

Cons

  • Hard to scale specific modules
  • One bug can affect the entire app
  • Deployments become riskier as code base grows

Real-world example

Early versions of Twitter or Shopify where business logic, UI, and data access shipped together.

Client-Server Architecture

Divides responsibilities between clients (requesters) and servers (providers) enabling centralized control of services and data.

When it fits best

  • Web and mobile applications
  • Systems requiring centralized data management
  • Apps with multiple client types accessing shared backend

Pros

  • Centralized security and updates
  • Thin clients with minimal processing
  • Well-understood pattern

Cons

  • Server is a potential bottleneck
  • Requires reliable network connectivity
  • Single point of failure without redundancy

Real-world example

Banking apps where mobile clients securely interact with backend services to retrieve balances or execute transactions.

Serverless Architecture

Cloud provider manages infrastructure; developers deploy event-driven functions that run on demand and scale automatically.

When it fits best

  • Unpredictable or bursty workloads
  • Rapid prototyping and micro APIs
  • Event-driven business logic

Pros

  • No server maintenance
  • Pay per execution
  • Instant scaling

Cons

  • Cold starts can add latency
  • Limited execution time and resources per function
  • Vendor lock-in and monitoring complexity

Real-world example

Image uploads trigger AWS Lambda to generate thumbnails and send notifications without provisioning servers.

Space-Based Architecture

Distributes data and processing across an in-memory data grid to eliminate database bottlenecks and enable massive throughput.

When it fits best

  • High-volume transaction systems
  • Real-time analytics
  • Financial trading and gaming platforms

Pros

  • Extreme scalability
  • Resilient to node failures
  • Low-latency processing

Cons

  • Higher memory cost
  • Complex to design and monitor
  • Not ideal for strong consistency requirements

Real-world example

Stock trading engines processing thousands of orders per second across an elastic grid.

Pipe-and-Filter Architecture

Data flows through a sequence of independent filters linked by pipes, with each filter performing a discrete transformation.

When it fits best

  • Data transformation pipelines
  • Media encoding and processing
  • Compilers and ETL workflows

Pros

  • Modular and testable filters
  • Encourages reusability
  • Easy to rearrange or add processing steps

Cons

  • Potential performance overhead between filters
  • Not suitable when filters share state
  • Complexity grows with branching pipes

Real-world example

Video processing pipeline that transcodes, watermarks, and compresses media through chained filters.

Peer-to-Peer Architecture

Nodes act as both clients and servers, sharing resources directly without centralized coordination.

When it fits best

  • File sharing (BitTorrent)
  • Blockchain networks
  • Decentralized communication tools

Pros

  • High scalability and fault tolerance
  • No central server dependency
  • Resource sharing distributes load

Cons

  • Security and trust are harder to enforce
  • Data consistency challenges
  • Performance depends on peer availability

Real-world example

Blockchain systems where nodes validate and propagate transactions collectively.

Hexagonal Architecture (Ports & Adapters)

Separates domain logic from external systems using ports (interfaces) and adapters, keeping the core independent of delivery mechanisms.

When it fits best

  • Domain-driven designs
  • Systems needing multiple interfaces (REST, CLI, messaging)
  • Projects emphasizing long-term maintainability

Pros

  • Core logic is framework-agnostic
  • High testability
  • Easy to swap infrastructure components

Cons

  • Requires architectural discipline
  • May feel overkill for simple apps
  • Initial learning curve

Real-world example

Applications that support web, mobile, and messaging integrations without altering domain logic.

How to choose the right pattern

Assess scope and complexity

Smaller MVPs thrive on monolithic or layered patterns. For complex systems and multiple squads, consider microservices, event-driven, or hexagonal designs.

Forecast scalability and demand

Expect unpredictable spikes? Match patterns like serverless, space-based, or microservices that elastically scale without costly rewrites.

Factor in team structure and expertise

Pick an architecture your team can operate confidently. A microservices approach without DevOps maturity can slow delivery more than help.

Consider deployment environment

Cloud-native? Serverless or event-driven can shine. Hybrid or on-prem scenarios might lean on layered, client-server, or hexagonal approaches.

Evaluate maintainability and evolution

If business rules change frequently, choose patterns that isolate domain logic (hexagonal) or allow incremental change (microservices).

Key takeaways

  • Architecture is a strategic decision—match it to your team, roadmap, and risk tolerance.
  • Favor patterns that let you adapt as user needs and traffic change.
  • Invest in automation, monitoring, and testing early; complex patterns amplify operational overhead.

Final thoughts

The perfect architecture balances engineering elegance with business outcomes. Start simple, evolve deliberately, and choose patterns that empower—not hinder—your teams.

At SA Systems we help you evaluate trade-offs, assemble the right building blocks, and launch with confidence. Architecture isn’t just structure—it’s your product’s long-term strategy.

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Israr Ahmed

Principal Software Architect at SA Systems

Israr works with founders and engineering leaders to design cloud architectures that scale, stay secure, and keep delivery velocity high.

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