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Asked 4 months ago
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Explain the trade-offs between using a monolithic architecture versus a microservices architecture. Give one example of a situation where a monolithic architecture might be preferable
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| Aspect | Monolithic Architecture | Microservices Architecture |
|---|---|---|
| Deployment | Single artifact; one deployable unit. | Independent services; each can be deployed separately. |
| Complexity | Simpler to develop, test, and run initially. | Higher operational complexity (service discovery, networking, monitoring). |
| Scalability | Scale the whole app even if only one part is hot. | Scale individual services based on demand, saving resources. |
| Team Autonomy | Larger codebase → tighter coordination among developers. | Small, bounded‑context services → teams can work independently. |
| Fault Isolation | A bug or resource leak can bring down the entire app. | Failures are contained to the offending service (if designed correctly). |
| Technology Diversity | Typically one tech stack for the whole system. | Each service can use the language, framework, or database that fits its needs. |
| Operational Overhead | Fewer moving parts → lower DevOps burden. | Need CI/CD pipelines, container orchestration, logging, tracing, etc. |
| Time‑to‑Market | Faster to get a MVP up and running. | Longer initial setup; benefits accrue as the system grows. |
Scenario: A startup is building its first product—a simple web‑based SaaS tool with a limited feature set and a small development team.
Once the product gains traction, the team can reassess and consider extracting high‑traffic or high‑complexity components into microservices.
| Aspect | Monolithic Architecture | Microservices Architecture |
|---|---|---|
| Deployment | Single artifact; one deploy per release. | Independent services; each can be deployed separately. |
| Complexity | Simpler codebase, easier to understand for small teams. | Higher operational complexity (service discovery, networking, monitoring). |
| Scalability | Scale the whole app even if only one component is hot. | Scale individual services based on demand, saving resources. |
| Fault Isolation | A failure in one part can bring down the entire app. | Failures are contained to the offending service. |
| Technology Stack | Typically one language/framework for the whole system. | Each service can use the best‑fit language, DB, or framework. |
| Team Autonomy | Teams share the same codebase → more coordination. | Teams own their services → can work in parallel with fewer bottlenecks. |
| Testing & CI/CD | Easier to run end‑to‑end tests on a single binary. | Requires contract testing, integration testing across services. |
| Operational Overhead | Lower (single process, single DB). | Higher (multiple runtimes, containers, orchestration, logging). |
| Time‑to‑Market | Faster for simple or MVP projects. | Slower initial setup; payoff appears as the system grows. |
Scenario: A startup is building an MVP for a web‑based SaaS product with a small development team (2‑4 engineers) and expects modest traffic for the first 12‑18 months.
Once the product validates the market and traffic patterns become more demanding, the team can consider refactoring critical components into microservices.
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