Application Layer: Monolith vs Microservices — When to Split and What It Actually Costs

Every production system starts with the same architectural question: one deployable unit or many? Get it wrong early and you'll either drown in distributed complexity you didn't need, or hit scaling walls you can't escape. This video breaks down the real trade-offs with concrete warning signs, failure patterns, and guidance for working with AI coding agents in both architectures.

In this video, you'll learn:

MONOLITH ARCHITECTURE
• Why "Monolith First" is the industry consensus from Martin Fowler, Sam Newman, and DHH
• Full ACID transactions across all modules — a guarantee you lose the moment you split
• The "Majestic Monolith" pattern: how Basecamp and Shopify scale massive products on a single codebase
• Optimal team size: 2-8 engineers before coordination bottlenecks appear

MONOLITH TRADE-OFFS
• 100% blast radius — one fatal error in any module takes down everything
• In-process function calls vs. redeploying the entire app for a one-line fix
• Why you should organize into clear modules with explicit interfaces from day one

THE 6 WARNING SIGNS TO SPLIT
• Deployment bottlenecks, team size beyond 8-10, divergent scaling needs
• Technology diversity requirements (Python ML, Go throughput, Node.js real-time)
• PCI-DSS/HIPAA compliance isolation and stable domain seams
• Conway's Law: why team structure is a legitimate architectural signal

MICROSERVICES ARCHITECTURE
• API Gateway pattern: centralized routing, auth, and rate limiting
• Per-service databases — why shared databases are the #1 migration failure mode
• Asynchronous communication via message queues for fault isolation

THE COMPLEXITY TAX
• Network failures, distributed tracing, service discovery (Kubernetes DNS, Consul)
• Cross-service consistency: the Saga pattern and compensating transactions
• Operational overhead: every service multiplies pipelines, dashboards, and runbooks
• Service mesh tools (Istio, Linkerd) — what they solve and what they add

CIRCUIT BREAKER PATTERN
• Three states: Closed, Open, Half-Open — preventing cascading failures
• How Amazon Prime Video moved back to a monolith and cut costs by 90%
• Why architecture should fit the problem, not the trend

AI AGENT PROMPTING
• Monolith: specify module, data access pattern, and shared DB connection
• Extraction: require owned database tables and API contract definition first
• Microservices: specify async communication pattern and network error handling
• The pattern: AI doesn't know your architecture unless you explicitly tell it

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Видео Application Layer: Monolith vs Microservices — When to Split and What It Actually Costs канала WiseBuilder