Microservices promise scalability, but real numbers show they add latency, complexity, and failure points. A simple e‑commerce checkout takes 156 ms in a monolith versus 180 ms when split into six services, a 15 % slowdown that scales with every added call.

Network overhead dominates: a single HTTP request inside a data center takes 1–5 ms, thousands of times slower than an in‑process call. Serialization, TLS handshakes, and retries add another millisecond per hop, turning a 3 ms service call into a 3,000‑fold delay. This latency ripple forces developers to rethink service boundaries.

Reliability suffers too. With five 99.9 %‑up services chained, overall availability drops to 99.5 %, translating to 3.6 hours of downtime per month versus 43 minutes for a monolith. Cascading failures, circuit breakers, and retries amplify the problem, making distributed systems harder to maintain and increasing operational costs for teams.

Despite these drawbacks, microservices shine when teams exceed 50 engineers or when services demand distinct scaling, technology stacks, or compliance isolation. In such cases, modular monoliths offer a middle ground, preserving performance while granting clear boundaries and independent deployment without the overhead of full microservice stacks for modern.