Monitoring a microservices system is a different problem from monitoring one app, because the failures live between the services, not inside them.
Microservices monitoring is the practice of observing the health, performance and interactions of the many small, independent services that make up a microservices application, so you can find and fix problems that span services, which single-application monitoring cannot.
In a monolith, a request runs in one process you can watch directly. In a microservices architecture, a single user request fans out across many services, each with its own metrics and logs, and the interesting failures are between them: a slow dependency, a retry storm, a cascading timeout. Watching each service in isolation gives a misleading picture, because a service can look healthy while the request it is part of is failing three hops away. This is why distributed tracing, which follows a request across every service, is essential to microservices monitoring rather than optional.
A good microservices monitoring setup captures three things. Per service, the golden signals or the RED method, request rate, error rate and latency, so each service's own health is clear. Across services, the dependency map or call graph, derived from traces, so you can see how services connect and where latency or errors originate. And underneath, the infrastructure, containers and Kubernetes, on which the services run. The combination lets you move from a symptom (the checkout is slow) to a cause (this downstream service is timing out) instead of guessing.
OpenTelemetry has become the standard for microservices observability precisely because the hard part is crossing service boundaries. Instrumenting each service with OpenTelemetry, and propagating trace context on every call between services, produces end-to-end traces that stitch the whole request together, and unifies metrics, logs and traces in one pipeline. A service map generated from those traces reflects how the system actually behaves in production, which a hand-drawn architecture diagram never does. Instrument once, trace across, is the shape of modern microservices monitoring.
Ops Singularity ingests microservice telemetry over OpenTelemetry, correlates it across the service graph, and Sentinel AI resolves the cross-service incidents it reveals, a failing dependency, a retry storm, through governed Action Tickets, so a problem that spans services leads to resolution rather than a per-service dashboard hunt.
Because a request crosses many services and the failures live between them. Per-service metrics can look healthy while the overall request fails, so you need distributed tracing to follow the request across service boundaries.
A monolith runs in one process you can watch directly; microservices spread a request across many independent services, making the dependency graph and distributed tracing central to understanding behaviour.
Not strictly, but OpenTelemetry is the standard for it, providing the instrumentation and context propagation that produce end-to-end traces across service boundaries.
Ops Singularity turns open telemetry into autonomous, governed resolution. See it on your own stack.