service-mesh-tradeoffslisted

# Service mesh tradeoffs A service mesh moves cross-cutting service-to-service concerns (mTLS, retries, traffic shifting, observability) out of application code and into sidecar proxies. The value is real and so is the cost: a mesh is a distributed system you now operate on top of your distributed system. Adopt it for specific needs, not for completeness. ## Method 1. **Know what the mesh actually gives you.** Automatic mTLS between services (see zero-trust-basics), uniform retries/timeouts/circuit-breaking without app changes (see timeouts-and-retries), traffic shifting for canary/blue-green (see canary-analysis), and consistent east-west telemetry (see distributed-tracing): all applied to service-to-service traffic without touching application code. That last part is the real pitch: policy without per-service reimplementation. 2. **Price the operational cost honestly.** A sidecar per pod (latency per hop, memory, CPU), a control plane to run and upgrade, added failure modes (the proxy is now in every request path), debugging that must account for the mesh, and a real learning curve. This is not free infrastructure; it is a platform commitment with a team cost (see managed-vs-selfhosted's TCO honesty). 3. **Weigh the library alternative.** For a small number of services in one language, a shared library (resilience, mTLS, telemetry: the Netflix-OSS-style approach) delivers much of the value without sidecars: cheaper to ru